# MIT License
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# Copyright (c) 2022 CNRS
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from typing import Optional

from torch.optim import Optimizer
from torch.optim.lr_scheduler import (
    CosineAnnealingWarmRestarts as _CosineAnnealingWarmRestarts,
)


def CosineAnnealingWarmRestarts(
    optimizer: Optimizer,
    min_lr: float = 1e-8,
    max_lr: float = 1e-3,
    patience: int = 1,
    num_batches_per_epoch: Optional[int] = None,
    **kwargs,
):
    """Wrapper around CosineAnnealingWarmRestarts

    Parameters
    ----------
    optimizer : Optimizer
        Optimizer
    min_lr : float, optional
        Defaults to 1e-8.
    max_lr : float, optional
        Defaults to 1e-3
    patience : int, optional
        Number of epochs per cycle. Defaults to 1.
    num_batches_per_epoch : int, optional
        Number of batches per epoch.
    """

    # initialize optimizer lr to max_lr
    for g in optimizer.param_groups:
        g["lr"] = max_lr

    num_steps = patience * num_batches_per_epoch

    return {
        "scheduler": _CosineAnnealingWarmRestarts(
            optimizer, num_steps, eta_min=min_lr, T_mult=2
        ),
        "interval": "step",
    }