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from __future__ import annotations
from collections.abc import Collection, Callable, Sequence
from typing import Any, Protocol, Union, TypeVar, runtime_checkable
from numpy import (
ndarray,
dtype,
generic,
bool_,
unsignedinteger,
integer,
floating,
complexfloating,
number,
timedelta64,
datetime64,
object_,
void,
str_,
bytes_,
)
from ._nested_sequence import _NestedSequence
_T = TypeVar("_T")
_ScalarType = TypeVar("_ScalarType", bound=generic)
_ScalarType_co = TypeVar("_ScalarType_co", bound=generic, covariant=True)
_DType = TypeVar("_DType", bound=dtype[Any])
_DType_co = TypeVar("_DType_co", covariant=True, bound=dtype[Any])
NDArray = ndarray[Any, dtype[_ScalarType_co]]
# The `_SupportsArray` protocol only cares about the default dtype
# (i.e. `dtype=None` or no `dtype` parameter at all) of the to-be returned
# array.
# Concrete implementations of the protocol are responsible for adding
# any and all remaining overloads
@runtime_checkable
class _SupportsArray(Protocol[_DType_co]):
def __array__(self) -> ndarray[Any, _DType_co]: ...
@runtime_checkable
class _SupportsArrayFunc(Protocol):
"""A protocol class representing `~class.__array_function__`."""
def __array_function__(
self,
func: Callable[..., Any],
types: Collection[type[Any]],
args: tuple[Any, ...],
kwargs: dict[str, Any],
) -> object: ...
# TODO: Wait until mypy supports recursive objects in combination with typevars
_FiniteNestedSequence = Union[
_T,
Sequence[_T],
Sequence[Sequence[_T]],
Sequence[Sequence[Sequence[_T]]],
Sequence[Sequence[Sequence[Sequence[_T]]]],
]
# A subset of `npt.ArrayLike` that can be parametrized w.r.t. `np.generic`
_ArrayLike = Union[
_SupportsArray[dtype[_ScalarType]],
_NestedSequence[_SupportsArray[dtype[_ScalarType]]],
]
# A union representing array-like objects; consists of two typevars:
# One representing types that can be parametrized w.r.t. `np.dtype`
# and another one for the rest
_DualArrayLike = Union[
_SupportsArray[_DType],
_NestedSequence[_SupportsArray[_DType]],
_T,
_NestedSequence[_T],
]
# TODO: support buffer protocols once
#
# https://bugs.python.org/issue27501
#
# is resolved. See also the mypy issue:
#
# https://github.com/python/typing/issues/593
ArrayLike = _DualArrayLike[
dtype[Any],
Union[bool, int, float, complex, str, bytes],
]
# `ArrayLike<X>_co`: array-like objects that can be coerced into `X`
# given the casting rules `same_kind`
_ArrayLikeBool_co = _DualArrayLike[
dtype[bool_],
bool,
]
_ArrayLikeUInt_co = _DualArrayLike[
dtype[Union[bool_, unsignedinteger[Any]]],
bool,
]
_ArrayLikeInt_co = _DualArrayLike[
dtype[Union[bool_, integer[Any]]],
Union[bool, int],
]
_ArrayLikeFloat_co = _DualArrayLike[
dtype[Union[bool_, integer[Any], floating[Any]]],
Union[bool, int, float],
]
_ArrayLikeComplex_co = _DualArrayLike[
dtype[Union[
bool_,
integer[Any],
floating[Any],
complexfloating[Any, Any],
]],
Union[bool, int, float, complex],
]
_ArrayLikeNumber_co = _DualArrayLike[
dtype[Union[bool_, number[Any]]],
Union[bool, int, float, complex],
]
_ArrayLikeTD64_co = _DualArrayLike[
dtype[Union[bool_, integer[Any], timedelta64]],
Union[bool, int],
]
_ArrayLikeDT64_co = Union[
_SupportsArray[dtype[datetime64]],
_NestedSequence[_SupportsArray[dtype[datetime64]]],
]
_ArrayLikeObject_co = Union[
_SupportsArray[dtype[object_]],
_NestedSequence[_SupportsArray[dtype[object_]]],
]
_ArrayLikeVoid_co = Union[
_SupportsArray[dtype[void]],
_NestedSequence[_SupportsArray[dtype[void]]],
]
_ArrayLikeStr_co = _DualArrayLike[
dtype[str_],
str,
]
_ArrayLikeBytes_co = _DualArrayLike[
dtype[bytes_],
bytes,
]
_ArrayLikeInt = _DualArrayLike[
dtype[integer[Any]],
int,
]
# Extra ArrayLike type so that pyright can deal with NDArray[Any]
# Used as the first overload, should only match NDArray[Any],
# not any actual types.
# https://github.com/numpy/numpy/pull/22193
class _UnknownType:
...
_ArrayLikeUnknown = _DualArrayLike[
dtype[_UnknownType],
_UnknownType,
]
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