import torch.nn as nn
from itertools import repeat
from typing import Iterable


def _ntuple(n):
    """Copy item to be a tuple with n length (Implemented as timm)
    """

    def parse(x):
        if isinstance(x, Iterable):
            return x
        else:
            return tuple(repeat(x, n))

    return parse


to_2tuple = _ntuple(2)
to_ntuple = _ntuple


class DropPath(nn.Module):
    """Stochasticly zero channels of data.(Implemented as timm)
    """

    def __init__(self, drop=0.5, scale=True):
        super().__init__()
        self.drop = drop
        self.scale = scale

    def forward(self, x):
        return self.drop_path(x, self.drop, self.training, self.scale)

    def drop_path(self, x, drop=0.5, training=True, scale=True):
        if drop == 0. or not training:
            return x
        drop_p = 1 - drop
        shape = (x.shape[0],) + (1,) * (x.ndim - 1)
        random_tensor = x.new_empty(shape).bernoulli_(drop_p)
        if drop_p > 0. and scale:
            random_tensor.div_(drop_p)

        return x * random_tensor