# Copyright (c) 2023-2024, NVIDIA CORPORATION.  All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto.  Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.

from logging import getLogger
import math
import os
from typing import Dict, List, Optional, Union, Tuple
from types import MethodType

import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.utils import parametrize


# For now, don't do anything
class DAMP(nn.Identity):
    def __init__(self, std: float):
        super().__init__()
        self.std = std


def enable_damp(model: nn.Module, std: float):
    if isinstance(model, (list, tuple)):
        for m in model:
            enable_damp(m, std)
        return

    for name, module in model.named_modules():
        if isinstance(module, nn.Linear):
            parametrize.register_parametrization(module, 'weight', DAMP(std))


def configure_damp_from_args(model: nn.Module, args):
    damp = getattr(args, 'damp', None)
    if damp:
        enable_damp(model, damp)