# MIT License
#
# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
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# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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from torch.optim.lr_scheduler import _LRScheduler


class LearningRateScheduler(_LRScheduler):
    r"""
    Provides inteface of learning rate scheduler.

    Note:
        Do not use this class directly, use one of the sub classes.
    """

    def __init__(self, optimizer, init_lr):
        self.optimizer = optimizer
        self.init_lr = init_lr

    def step(self, *args, **kwargs):
        raise NotImplementedError

    @staticmethod
    def set_lr(optimizer, lr):
        for g in optimizer.param_groups:
            g["lr"] = lr

    def get_lr(self):
        for g in self.optimizer.param_groups:
            return g["lr"]