from __future__ import annotations

from typing import TYPE_CHECKING
if TYPE_CHECKING:
    import torch
    array = torch.Tensor
    from torch import dtype as Dtype
    from typing import Optional

from torch.linalg import *

# torch.linalg doesn't define __all__
# from torch.linalg import __all__ as linalg_all
from torch import linalg as torch_linalg
linalg_all = [i for i in dir(torch_linalg) if not i.startswith('_')]

# outer is implemented in torch but aren't in the linalg namespace
from torch import outer
from ._aliases import _fix_promotion, matrix_transpose, tensordot, sum

# Note: torch.linalg.cross does not default to axis=-1 (it defaults to the
# first axis with size 3), see https://github.com/pytorch/pytorch/issues/58743
def cross(x1: array, x2: array, /, *, axis: int = -1) -> array:
    x1, x2 = _fix_promotion(x1, x2, only_scalar=False)
    return torch_linalg.cross(x1, x2, dim=axis)

def vecdot(x1: array, x2: array, /, *, axis: int = -1, **kwargs) -> array:
    from ._aliases import isdtype

    x1, x2 = _fix_promotion(x1, x2, only_scalar=False)

    # torch.linalg.vecdot doesn't support integer dtypes
    if isdtype(x1.dtype, 'integral') or isdtype(x2.dtype, 'integral'):
        if kwargs:
            raise RuntimeError("vecdot kwargs not supported for integral dtypes")
        ndim = max(x1.ndim, x2.ndim)
        x1_shape = (1,)*(ndim - x1.ndim) + tuple(x1.shape)
        x2_shape = (1,)*(ndim - x2.ndim) + tuple(x2.shape)
        if x1_shape[axis] != x2_shape[axis]:
            raise ValueError("x1 and x2 must have the same size along the given axis")

        x1_, x2_ = torch.broadcast_tensors(x1, x2)
        x1_ = torch.moveaxis(x1_, axis, -1)
        x2_ = torch.moveaxis(x2_, axis, -1)

        res = x1_[..., None, :] @ x2_[..., None]
        return res[..., 0, 0]
    return torch.linalg.vecdot(x1, x2, dim=axis, **kwargs)

def solve(x1: array, x2: array, /, **kwargs) -> array:
    x1, x2 = _fix_promotion(x1, x2, only_scalar=False)
    return torch.linalg.solve(x1, x2, **kwargs)

# torch.trace doesn't support the offset argument and doesn't support stacking
def trace(x: array, /, *, offset: int = 0, dtype: Optional[Dtype] = None) -> array:
    # Use our wrapped sum to make sure it does upcasting correctly
    return sum(torch.diagonal(x, offset=offset, dim1=-2, dim2=-1), axis=-1, dtype=dtype)

__all__ = linalg_all + ['outer', 'trace', 'matrix_transpose', 'tensordot',
                        'vecdot', 'solve']

del linalg_all