# Copyright 2022 The Plenoptix Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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"""Scheduler Classes"""

import numpy as np
from torch.optim.lr_scheduler import LambdaLR


class ExponentialDecaySchedule(LambdaLR):
    """Exponential learning rate decay function.
    See https://github.com/google-research/google-research/blob/
    fd2cea8cdd86b3ed2c640cbe5561707639e682f3/jaxnerf/nerf/utils.py#L360
    for details.

    Args:
        optimizer: The optimizer to update.
        lr_init: The initial learning rate.
        lr_final: The final learning rate.
        max_steps: The maximum number of steps.
        lr_delay_steps: The number of steps to delay the learning rate. Default is 0.
        lr_delay_mult: The multiplier for the learning rate after the delay. Default is 1.0.
    """

    def __init__(self, optimizer, lr_init, lr_final, max_steps, lr_delay_steps=0, lr_delay_mult=1.0) -> None:
        def func(step):
            if lr_delay_steps > 0:
                delay_rate = lr_delay_mult + (1 - lr_delay_mult) * np.sin(
                    0.5 * np.pi * np.clip(step / lr_delay_steps, 0, 1)
                )
            else:
                delay_rate = 1.0
            t = np.clip(step / max_steps, 0, 1)
            log_lerp = np.exp(np.log(lr_init) * (1 - t) + np.log(lr_final) * t)
            multiplier = (
                log_lerp / lr_init
            )  # divided by lr_init because the multiplier is with the initial learning rate
            return delay_rate * multiplier

        super().__init__(optimizer, lr_lambda=func)