from torch._six import container_abcs
from itertools import repeat


def _ntuple(n):
    def parse(x):
        if isinstance(x, container_abcs.Iterable):
            return x
        return tuple(repeat(x, n))
    return parse

_single = _ntuple(1)
_pair = _ntuple(2)
_triple = _ntuple(3)
_quadruple = _ntuple(4)


def _repeat_tuple(t, n):
    r"""Repeat each element of `t` for `n` times.

    This can be used to translate padding arg used by Conv and Pooling modules
    to the ones used by `F.pad`.
    """
    return tuple(x for x in t for _ in range(n))


def _list_with_default(out_size, defaults):
    # type: (List[int], List[int]) -> List[int]
    if isinstance(out_size, int):
        return out_size
    if len(defaults) <= len(out_size):
        raise ValueError('Input dimension should be at least {}'.format(len(out_size) + 1))
    return [v if v is not None else d for v, d in zip(out_size, defaults[-len(out_size):])]
