import torch

def unitwise_norm(x, norm_type=2.0):
    if x.ndim <= 1:
        return x.norm(norm_type)
    else:
        # works for nn.ConvNd and nn,Linear where output dim is first in the kernel/weight tensor
        # might need special cases for other weights (possibly MHA) where this may not be true
        return x.norm(norm_type, dim=tuple(range(1, x.ndim)), keepdim=True)


def adaptive_clip_grad(parameters, clip_factor=0.01, eps=1e-3, norm_type=2.0):
    if isinstance(parameters, torch.Tensor):
        parameters = [parameters]
    for p in parameters:
        if p.grad is None:
            continue
        p_data = p.detach()
        g_data = p.grad.detach()
        max_norm = unitwise_norm(p_data, norm_type=norm_type).clamp_(min=eps).mul_(clip_factor)
        grad_norm = unitwise_norm(g_data, norm_type=norm_type)
        clipped_grad = g_data * (max_norm / grad_norm.clamp(min=1e-6))
        new_grads = torch.where(grad_norm < max_norm, g_data, clipped_grad)
        p.grad.detach().copy_(new_grads)

def dispatch_clip_grad(parameters, value: float, mode: str = 'norm', norm_type: float = 2.0):
    """ Dispatch to gradient clipping method
    Args:
        parameters (Iterable): model parameters to clip
        value (float): clipping value/factor/norm, mode dependant
        mode (str): clipping mode, one of 'norm', 'value', 'agc'
        norm_type (float): p-norm, default 2.0
    """
    if mode == 'norm':
        torch.nn.utils.clip_grad_norm_(parameters, value, norm_type=norm_type)
    elif mode == 'value':
        torch.nn.utils.clip_grad_value_(parameters, value)
    elif mode == 'agc':
        adaptive_clip_grad(parameters, value, norm_type=norm_type)
    else:
        assert False, f"Unknown clip mode ({mode})."