from typing import Tuple

from torch import nn
from torch.nn.common_types import _size_2_t


def auto_pad(kernel_size: _size_2_t, dilation: _size_2_t = 1, **kwargs) -> Tuple[int, int]:
    """
    Auto Padding for the convolution blocks
    """
    if isinstance(kernel_size, int):
        kernel_size = (kernel_size, kernel_size)
    if isinstance(dilation, int):
        dilation = (dilation, dilation)

    pad_h = ((kernel_size[0] - 1) * dilation[0]) // 2
    pad_w = ((kernel_size[1] - 1) * dilation[1]) // 2
    return (pad_h, pad_w)


def get_activation(activation: str) -> nn.Module:
    """
    Retrieves an activation function from the PyTorch nn module based on its name, case-insensitively.
    """
    if not activation or activation.lower() in ["false", "none"]:
        return nn.Identity()

    activation_map = {
        name.lower(): obj
        for name, obj in nn.modules.activation.__dict__.items()
        if isinstance(obj, type) and issubclass(obj, nn.Module)
    }
    if activation.lower() in activation_map:
        return activation_map[activation.lower()]()
    else:
        raise ValueError(f"Activation function '{activation}' is not found in torch.nn")