# MIT License
#
# Copyright (c) 2021 CNRS
#
# 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
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# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
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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
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# SOFTWARE.


from torch.optim import Optimizer
from torch.optim.lr_scheduler import CyclicLR as _CyclicLR


def CyclicLR(
    optimizer: Optimizer,
    min_lr: float = 1e-8,
    max_lr: float = 1e-3,
    step_size_up: int = 50000,
    **kwargs,
):
    """Wrapper around CyclicLR learning rate scheduler

    Parameters
    ----------
    optimizer : Optimizer
        Optimizer
    min_lr : float, optional
        Defaults to 1e-8.
    max_lr : float, optional
        Defaults to 1e-3
    step_size_up : int, optional
        Number of training iterations in the increasing half of a cycle.
        Defaults to 50000.
    """

    return {
        "scheduler": _CyclicLR(
            optimizer,
            base_lr=min_lr,
            max_lr=max_lr,
            step_size_up=step_size_up,
            mode="triangular2",
            cycle_momentum=False,
        ),
        "interval": "step",
    }

@@ -1312,16 +1312,46 @@
  50000,%0A
+    mode: str = %22triangular%22,%0A
     **kw
@@ -1731,16 +1731,101 @@
  50000.%0A
+    mode : %7B%22triangular%22, %22triangular2%22%7D, optional%0A        Defaults to %22triangular%22.%0A
     %22%22%22%0A
@@ -2004,29 +2004,20 @@
    mode=
-%22triangular2%22
+mode
 ,%0A      

switch to triangular