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144 lines
3.7 KiB
144 lines
3.7 KiB
from collections.abc import Sequence
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from typing import TypeVar, Any, overload, Union, Literal
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from numpy import (
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ndarray,
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dtype,
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bool_,
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unsignedinteger,
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signedinteger,
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floating,
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complexfloating,
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number,
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_OrderKACF,
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)
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from numpy._typing import (
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_ArrayLikeBool_co,
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_ArrayLikeUInt_co,
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_ArrayLikeInt_co,
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_ArrayLikeFloat_co,
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_ArrayLikeComplex_co,
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_DTypeLikeBool,
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_DTypeLikeUInt,
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_DTypeLikeInt,
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_DTypeLikeFloat,
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_DTypeLikeComplex,
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_DTypeLikeComplex_co,
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)
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_ArrayType = TypeVar(
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"_ArrayType",
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bound=ndarray[Any, dtype[Union[bool_, number[Any]]]],
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)
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_OptimizeKind = None | bool | Literal["greedy", "optimal"] | Sequence[Any]
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_CastingSafe = Literal["no", "equiv", "safe", "same_kind"]
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_CastingUnsafe = Literal["unsafe"]
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__all__: list[str]
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# TODO: Properly handle the `casting`-based combinatorics
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# TODO: We need to evaluate the content `__subscripts` in order
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# to identify whether or an array or scalar is returned. At a cursory
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# glance this seems like something that can quite easily be done with
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# a mypy plugin.
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# Something like `is_scalar = bool(__subscripts.partition("->")[-1])`
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@overload
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def einsum(
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subscripts: str | _ArrayLikeInt_co,
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/,
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*operands: _ArrayLikeBool_co,
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out: None = ...,
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dtype: None | _DTypeLikeBool = ...,
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order: _OrderKACF = ...,
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casting: _CastingSafe = ...,
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optimize: _OptimizeKind = ...,
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) -> Any: ...
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@overload
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def einsum(
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subscripts: str | _ArrayLikeInt_co,
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/,
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*operands: _ArrayLikeUInt_co,
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out: None = ...,
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dtype: None | _DTypeLikeUInt = ...,
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order: _OrderKACF = ...,
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casting: _CastingSafe = ...,
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optimize: _OptimizeKind = ...,
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) -> Any: ...
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@overload
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def einsum(
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subscripts: str | _ArrayLikeInt_co,
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/,
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*operands: _ArrayLikeInt_co,
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out: None = ...,
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dtype: None | _DTypeLikeInt = ...,
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order: _OrderKACF = ...,
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casting: _CastingSafe = ...,
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optimize: _OptimizeKind = ...,
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) -> Any: ...
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@overload
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def einsum(
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subscripts: str | _ArrayLikeInt_co,
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/,
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*operands: _ArrayLikeFloat_co,
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out: None = ...,
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dtype: None | _DTypeLikeFloat = ...,
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order: _OrderKACF = ...,
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casting: _CastingSafe = ...,
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optimize: _OptimizeKind = ...,
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) -> Any: ...
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@overload
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def einsum(
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subscripts: str | _ArrayLikeInt_co,
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/,
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*operands: _ArrayLikeComplex_co,
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out: None = ...,
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dtype: None | _DTypeLikeComplex = ...,
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order: _OrderKACF = ...,
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casting: _CastingSafe = ...,
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optimize: _OptimizeKind = ...,
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) -> Any: ...
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@overload
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def einsum(
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subscripts: str | _ArrayLikeInt_co,
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/,
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*operands: Any,
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casting: _CastingUnsafe,
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dtype: None | _DTypeLikeComplex_co = ...,
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out: None = ...,
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order: _OrderKACF = ...,
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optimize: _OptimizeKind = ...,
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) -> Any: ...
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@overload
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def einsum(
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subscripts: str | _ArrayLikeInt_co,
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/,
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*operands: _ArrayLikeComplex_co,
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out: _ArrayType,
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dtype: None | _DTypeLikeComplex_co = ...,
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order: _OrderKACF = ...,
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casting: _CastingSafe = ...,
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optimize: _OptimizeKind = ...,
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) -> _ArrayType: ...
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@overload
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def einsum(
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subscripts: str | _ArrayLikeInt_co,
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/,
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*operands: Any,
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out: _ArrayType,
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casting: _CastingUnsafe,
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dtype: None | _DTypeLikeComplex_co = ...,
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order: _OrderKACF = ...,
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optimize: _OptimizeKind = ...,
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) -> _ArrayType: ...
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# NOTE: `einsum_call` is a hidden kwarg unavailable for public use.
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# It is therefore excluded from the signatures below.
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# NOTE: In practice the list consists of a `str` (first element)
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# and a variable number of integer tuples.
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def einsum_path(
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subscripts: str | _ArrayLikeInt_co,
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/,
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*operands: _ArrayLikeComplex_co,
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optimize: _OptimizeKind = ...,
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) -> tuple[list[Any], str]: ...
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