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__init__.pyi
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284 lines (254 loc) · 9.62 KB
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from typing import Any, Union, Literal, Optional, TypeAlias
# A lot of annotation features a depend on the Python version:
# - `typing.TypeAlias` Type aliases are supported from Python 3.10 to 3.11
# - `type` Type statements are supported from Python 3.12, replacing `typing.TypeAlias`
# - `typing.Literal` Literal types are supported from Python 3.8
#
# We can't and shouldn't use a different `.pyi` file for every single Python version.
# So let's assume we are targeting Python 3.11 and we have NumPy available.
from numpy.typing import NDArray
_BufferType: TypeAlias = Union[NDArray[Any], memoryview]
_MetricType = Literal[
"euclidean",
"sqeuclidean",
"inner",
"dot",
"cosine",
"cos",
"hamming",
"jaccard",
"kullbackleibler",
"kl",
"jensenshannon",
"js",
"intersection",
"bilinear",
"mahalanobis",
"fma",
"wsum",
]
_IntegralType = Literal[
"bin8",
# Signed integers
"int8",
"int16",
"int32",
"int64",
# Unsigned integers
"uint8",
"uint16",
"uint32",
"uint64",
]
_FloatType = Literal[
"f32",
"float32",
"f16",
"float16",
"f64",
"float64",
"bf16", #! Not supported by NumPy
"bfloat16", #! Not supported by NumPy
]
_ComplexType = Literal[
"complex32", #! Not supported by NumPy
"bcomplex32", #! Not supported by NumPy
"complex64",
"complex128",
]
class DistancesTensor(memoryview): ...
# ---------------------------------------------------------------------
# Controlling SIMD capabilities
def get_capabilities() -> dict[str, bool]: ...
def enable_capability(capability: str, /) -> None: ...
def disable_capability(capability: str, /) -> None: ...
# Accessing function pointers
def pointer_to_euclidean(dtype: Union[_IntegralType, _FloatType], /) -> int: ...
def pointer_to_sqeuclidean(dtype: Union[_IntegralType, _FloatType], /) -> int: ...
def pointer_to_cosine(dtype: Union[_IntegralType, _FloatType], /) -> int: ...
def pointer_to_inner(dtype: Union[_FloatType, _ComplexType], /) -> int: ...
def pointer_to_dot(dtype: Union[_FloatType, _ComplexType], /) -> int: ...
def pointer_to_vdot(dtype: Union[_FloatType, _ComplexType], /) -> int: ...
def pointer_to_hamming(dtype: _IntegralType, /) -> int: ...
def pointer_to_jaccard(dtype: _IntegralType, /) -> int: ...
def pointer_to_jensenshannon(dtype: _FloatType, /) -> int: ...
def pointer_to_kullbackleibler(dtype: _FloatType, /) -> int: ...
# ---------------------------------------------------------------------
# All pairwise distances, similar to: `scipy.spatial.distance.cdist`.
# https://docs.scipy.org/doc/scipy-1.11.4/reference/generated/scipy.spatial.distance.cdist.html
def cdist(
a: _BufferType,
b: _BufferType,
/,
metric: _MetricType = "euclidean",
*,
threads: int = 1,
dtype: Optional[Union[_IntegralType, _FloatType, _ComplexType]] = None,
out: Optional[_BufferType] = None,
out_dtype: Optional[Union[_FloatType, _ComplexType]] = None,
) -> Optional[Union[float, complex, DistancesTensor]]: ...
# ---------------------------------------------------------------------
# Vector-vector dot products for real and complex numbers
# ---------------------------------------------------------------------
# Inner product, similar to: `numpy.inner`.
# https://numpy.org/doc/stable/reference/generated/numpy.inner.html
def inner(
a: _BufferType,
b: _BufferType,
/,
dtype: Optional[Union[_FloatType, _ComplexType]] = None,
*,
out: Optional[_BufferType] = None,
out_dtype: Optional[Union[_FloatType, _ComplexType]] = None,
) -> Optional[Union[float, complex, DistancesTensor]]: ...
# Dot product, similar to: `numpy.dot`.
# https://numpy.org/doc/stable/reference/generated/numpy.dot.html
def dot(
a: _BufferType,
b: _BufferType,
/,
dtype: Optional[Union[_FloatType, _ComplexType]] = None,
*,
out: Optional[_BufferType] = None,
out_dtype: Union[_FloatType, _ComplexType] = None,
) -> Optional[Union[float, complex, DistancesTensor]]: ...
# Vector-vector dot product for complex conjugates, similar to: `numpy.vdot`.
# https://numpy.org/doc/stable/reference/generated/numpy.vdot.html
def vdot(
a: _BufferType,
b: _BufferType,
/,
dtype: Optional[_ComplexType] = None,
*,
out: Optional[Union[float, complex, DistancesTensor]] = None,
out_dtype: Optional[_ComplexType] = None,
) -> Optional[Union[complex, DistancesTensor]]: ...
# ---------------------------------------------------------------------
# Vector-vector spatial distance metrics for real and integer numbers
# ---------------------------------------------------------------------
# Vector-vector squared Euclidean distance, similar to: `scipy.spatial.distance.sqeuclidean`.
# https://docs.scipy.org/doc/scipy-1.11.4/reference/generated/scipy.spatial.distance.sqeuclidean.html
# https://numpy.org/doc/stable/reference/generated/numpy.linalg.norm.html
def sqeuclidean(
a: _BufferType,
b: _BufferType,
/,
dtype: Optional[Union[_IntegralType, _FloatType]] = None,
*,
out: Optional[_BufferType] = None,
out_dtype: Union[_FloatType] = None,
) -> Optional[Union[float, DistancesTensor]]: ...
# Vector-vector cosine distance, similar to: `scipy.spatial.distance.cosine`.
# https://docs.scipy.org/doc/scipy-1.11.4/reference/generated/scipy.spatial.distance.cosine.html
def cosine(
a: _BufferType,
b: _BufferType,
/,
dtype: Optional[Union[_IntegralType, _FloatType]] = None,
*,
out: Optional[_BufferType] = None,
out_dtype: Union[_FloatType] = None,
) -> Optional[Union[float, DistancesTensor]]: ...
# ---------------------------------------------------------------------
# Vector-vector similarity functions for binary vectors
# ---------------------------------------------------------------------
# Vector-vector Hamming distance, similar to: `scipy.spatial.distance.hamming`.
# https://docs.scipy.org/doc/scipy-1.11.4/reference/generated/scipy.spatial.distance.hamming.html
def hamming(
a: _BufferType,
b: _BufferType,
/,
dtype: Optional[_IntegralType] = None,
*,
out: Optional[_BufferType] = None,
out_dtype: Union[_FloatType] = None,
) -> Optional[Union[float, DistancesTensor]]: ...
# Vector-vector Jaccard distance, similar to: `scipy.spatial.distance.jaccard`.
# https://docs.scipy.org/doc/scipy-1.11.4/reference/generated/scipy.spatial.distance.jaccard.html
def jaccard(
a: _BufferType,
b: _BufferType,
/,
dtype: Optional[_IntegralType] = None,
*,
out: Optional[_BufferType] = None,
out_dtype: Union[_FloatType] = None,
) -> Optional[Union[float, DistancesTensor]]: ...
# ---------------------------------------------------------------------
# Vector-vector similarity between probability distributions
# ---------------------------------------------------------------------
# Vector-vector Jensen-Shannon distance, similar to: `scipy.spatial.distance.jensenshannon`.
# https://docs.scipy.org/doc/scipy-1.11.4/reference/generated/scipy.spatial.distance.jensenshannon.html
def jensenshannon(
a: _BufferType,
b: _BufferType,
/,
dtype: Optional[_FloatType] = None,
*,
out: Optional[_BufferType] = None,
out_dtype: Union[_FloatType] = None,
) -> Optional[Union[float, DistancesTensor]]: ...
# Vector-vector Kullback-Leibler divergence, similar to: `scipy.spatial.distance.kullback_leibler`.
# https://docs.scipy.org/doc/scipy-1.11.4/reference/generated/scipy.spatial.distance.kullback_leibler.html
def kullbackleibler(
a: _BufferType,
b: _BufferType,
/,
dtype: Optional[_FloatType] = None,
*,
out: Optional[_BufferType] = None,
out_dtype: Union[_FloatType] = None,
) -> Optional[Union[float, DistancesTensor]]: ...
# ---------------------------------------------------------------------
# Vector-vector similarity between vectors in curved spaces
# ---------------------------------------------------------------------
# Vector-vector bilinear distance, similar to: `numpy.dot(a, metric_tensor @ vector2)`.
# https://numpy.org/doc/stable/reference/generated/numpy.dot.html
def bilinear(
a: _BufferType,
b: _BufferType,
metric_tensor: _BufferType,
/,
dtype: Optional[_FloatType] = None,
) -> float: ...
# Vector-vector Mahalanobis distance, similar to: `scipy.spatial.distance.mahalanobis`.
# https://docs.scipy.org/doc/scipy-1.11.4/reference/generated/scipy.spatial.distance.mahalanobis.html
def mahalanobis(
a: _BufferType,
b: _BufferType,
inverse_covariance: _BufferType,
/,
dtype: Optional[_FloatType] = None,
) -> float: ...
# ---------------------------------------------------------------------
# Vector-vector similarity between sparse vectors
# ---------------------------------------------------------------------
# Vector-vector intersection similarity, similar to: `numpy.intersect1d`.
# https://numpy.org/doc/stable/reference/generated/numpy.intersect1d.html
def intersection(array1: _BufferType, array2: _BufferType, /) -> float: ...
# ---------------------------------------------------------------------
# Vector-vector math: FMA, WSum
# ---------------------------------------------------------------------
# Vector-vector element-wise fused-multiply add.
def fma(
a: _BufferType,
b: _BufferType,
c: _BufferType,
/,
dtype: Optional[Union[_FloatType, _IntegralType]] = None,
*,
alpha: float = 1,
beta: float = 1,
out: Optional[_BufferType] = None,
) -> Optional[DistancesTensor]: ...
# Vector-vector element-wise weighted sum.
def wsum(
a: _BufferType,
b: _BufferType,
/,
dtype: Optional[Union[_FloatType, _IntegralType]] = None,
*,
alpha: float = 1,
beta: float = 1,
out: Optional[_BufferType] = None,
) -> Optional[DistancesTensor]: ...