# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.

from typing import Iterable, Union

import torch


def get_grad_norm(
    parameters: Union[torch.Tensor, Iterable[torch.Tensor]], norm_type: float = 2.0
) -> torch.Tensor:
    """
    Adapted from: https://pytorch.org/docs/stable/_modules/torch/nn/utils/clip_grad.html#clip_grad_norm_
    """

    if isinstance(parameters, torch.Tensor):
        parameters = [parameters]
    parameters = [p for p in parameters if p.grad is not None]

    norm_type = float(norm_type)

    if len(parameters) == 0:
        return torch.tensor(0.0)

    device = parameters[0].grad.device

    total_norm = torch.norm(
        torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]),
        norm_type,
    )

    return total_norm