# mypy: allow-untyped-defs
r"""Learning Rate Scheduler."""
import math
import types
import warnings
from bisect import bisect_right
from collections import Counter
from collections.abc import Iterable, Sequence
from functools import partial, wraps
from typing import (
    Any,
    Callable,
    cast,
    Literal,
    Optional,
    SupportsFloat,
    TypedDict,
    Union,
)
from weakref import ref

from torch import inf, Tensor

from .optimizer import Optimizer


__all__ = [
    "LambdaLR",
    "MultiplicativeLR",
    "StepLR",
    "MultiStepLR",
    "ConstantLR",
    "LinearLR",
    "ExponentialLR",
    "SequentialLR",
    "CosineAnnealingLR",
    "ChainedScheduler",
    "ReduceLROnPlateau",
    "CyclicLR",
    "CosineAnnealingWarmRestarts",
    "OneCycleLR",
    "PolynomialLR",
    "LRScheduler",
]

EPOCH_DEPRECATION_WARNING = (
    "The epoch parameter in `scheduler.step()` was not necessary and is being "
    "deprecated where possible. Please use `scheduler.step()` to step the "
    "scheduler. During the deprecation, if epoch is different from None, the "
    "closed form is used instead of the new chainable form, where available. "
    "Please open an issue if you are unable to replicate your use case: "
    "https://github.com/pytorch/pytorch/issues/new/choose."
)


def _format_param(name: str, optimizer: Optimizer, param):
    """Return correctly formatted lr/momentum for each param group."""

    def _copy(_param):
        return _param.clone() if isinstance(_param, Tensor) else _param

    if isinstance(param, (list, tuple)):
        if len(param) != len(optimizer.param_groups):
            raise ValueError(
                f"{name} must have the same length as optimizer.param_groups. "
                f"{name} has {len(param)} values, param_groups has {len(optimizer.param_groups)}."
            )
    else:
        param = [param] * len(optimizer.param_groups)

    return list(map(_copy, param))


class LRScheduler:
    r"""Adjusts the learning rate during optimization."""

    _get_lr_called_within_step: bool = False

    def __init__(
        self,
        optimizer: Optimizer,
        last_epoch: int = -1,
    ):  # noqa: D107
        # Attach optimizer
        if not isinstance(optimizer, Optimizer):
            raise TypeError(f"{type(optimizer).__name__} is not an Optimizer")
        self.optimizer = optimizer

        # Initialize epoch and base learning rates
        if last_epoch == -1:
            for group in optimizer.param_groups:
                initial_lr = group["lr"]
                if isinstance(initial_lr, Tensor):
                    initial_lr = initial_lr.clone()
                group.setdefault("initial_lr", initial_lr)
        else:
            for i, group in enumerate(optimizer.param_groups):
                if "initial_lr" not in group:
                    raise KeyError(
                        "param 'initial_lr' is not specified "
                        f"in param_groups[{i}] when resuming an optimizer"
                    )
        self.base_lrs: list[float] = [
            group["initial_lr"] for group in optimizer.param_groups
        ]
        self.last_epoch = last_epoch

        # Following https://github.com/pytorch/pytorch/issues/20124
        # We would like to ensure that `lr_scheduler.step()` is called after
        # `optimizer.step()`
        def patch_track_step_called(opt: Optimizer):
            if hasattr(opt.step, "_wrapped_by_lr_sched"):
                # we've already patched
                return opt.step

            def wrap_step(step_fn):
                opt_ref = ref(self.optimizer)
                func = step_fn.__func__

                @wraps(func)
                def wrapper(*args, **kwargs):
                    opt = opt_ref()
                    opt._opt_called = True  # type: ignore[union-attr]
                    return func.__get__(opt, opt.__class__)(*args, **kwargs)

                wrapper._wrapped_by_lr_sched = True  # type: ignore[attr-defined]
                return wrapper

            opt.step = wrap_step(opt.step)  # type: ignore[method-assign]

        patch_track_step_called(self.optimizer)
        self._initial_step()

    def _initial_step(self):
        """Initialize step counts and perform a step."""
        self._step_count = 0
        self.step()

    def state_dict(self):
        """Return the state of the scheduler as a :class:`dict`.

        It contains an entry for every variable in self.__dict__ which
        is not the optimizer.
        """
        return {
            key: value for key, value in self.__dict__.items() if key != "optimizer"
        }

    def load_state_dict(self, state_dict: dict[str, Any]):
        """Load the scheduler's state.

        Args:
            state_dict (dict): scheduler state. Should be an object returned
                from a call to :meth:`state_dict`.
        """
        self.__dict__.update(state_dict)

    def get_last_lr(self) -> list[float]:
        """Return last computed learning rate by current scheduler."""
        return self._last_lr

    def get_lr(self) -> list[float]:
        """Compute learning rate using chainable form of the scheduler."""
        raise NotImplementedError

    def step(self, epoch: Optional[int] = None):
        """Perform a step."""
        # Raise a warning if old pattern is detected
        # https://github.com/pytorch/pytorch/issues/20124
        if self._step_count == 1:
            if not hasattr(self.optimizer.step, "_wrapped_by_lr_sched"):
                warnings.warn(
                    "Seems like `optimizer.step()` has been overridden after learning rate scheduler "
                    "initialization. Please, make sure to call `optimizer.step()` before "
                    "`lr_scheduler.step()`. See more details at "
                    "https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate",
                    UserWarning,
                )

            # Just check if there were two first lr_scheduler.step() calls before optimizer.step()
            elif not getattr(self.optimizer, "_opt_called", False):
                warnings.warn(
                    "Detected call of `lr_scheduler.step()` before `optimizer.step()`. "
                    "In PyTorch 1.1.0 and later, you should call them in the opposite order: "
                    "`optimizer.step()` before `lr_scheduler.step()`.  Failure to do this "
                    "will result in PyTorch skipping the first value of the learning rate schedule. "
                    "See more details at "
                    "https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate",
                    UserWarning,
                )
        self._step_count += 1

        with _enable_get_lr_call(self):
            if epoch is None:
                self.last_epoch += 1
                values = self.get_lr()
            else:
                warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
                self.last_epoch = epoch
                if hasattr(self, "_get_closed_form_lr"):
                    values = cast(list[float], self._get_closed_form_lr())
                else:
                    values = self.get_lr()

        for param_group, lr in zip(self.optimizer.param_groups, values):
            if isinstance(param_group["lr"], Tensor):
                param_group["lr"].fill_(lr)
            else:
                param_group["lr"] = lr

        self._last_lr: list[float] = [
            group["lr"] for group in self.optimizer.param_groups
        ]


def _warn_get_lr_called_within_step(lr_scheduler: LRScheduler):
    if not lr_scheduler._get_lr_called_within_step:
        warnings.warn(
            "To get the last learning rate computed by the scheduler, "
            "please use `get_last_lr()`.",
            UserWarning,
            stacklevel=2,
        )


# Including _LRScheduler for backwards compatibility
# Subclass instead of assign because we want __name__ of _LRScheduler to be _LRScheduler (assigning would make it LRScheduler).
class _LRScheduler(LRScheduler):
    pass


class _enable_get_lr_call:
    def __init__(self, o: LRScheduler):
        self.o = o

    def __enter__(self):
        self.o._get_lr_called_within_step = True
        return self

    def __exit__(self, type, value, traceback):
        self.o._get_lr_called_within_step = False


class LambdaLR(LRScheduler):
    """Sets the initial learning rate.

    The learning rate of each parameter group is set to the initial lr
    times a given function. When last_epoch=-1, sets initial lr as lr.

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        lr_lambda (function or list): A function which computes a multiplicative
            factor given an integer parameter epoch, or a list of such
            functions, one for each group in optimizer.param_groups.
        last_epoch (int): The index of last epoch. Default: -1.

    Example:
        >>> # xdoctest: +SKIP
        >>> # Assuming optimizer has two groups.
        >>> lambda1 = lambda epoch: epoch // 30
        >>> lambda2 = lambda epoch: 0.95 ** epoch
        >>> scheduler = LambdaLR(optimizer, lr_lambda=[lambda1, lambda2])
        >>> for epoch in range(100):
        >>>     train(...)
        >>>     validate(...)
        >>>     scheduler.step()
    """

    def __init__(
        self,
        optimizer: Optimizer,
        lr_lambda: Union[Callable[[int], float], list[Callable[[int], float]]],
        last_epoch: int = -1,
    ):  # noqa: D107
        self.optimizer = optimizer

        self.lr_lambdas: list[Callable[[int], float]]
        if not isinstance(lr_lambda, list) and not isinstance(lr_lambda, tuple):
            self.lr_lambdas = [lr_lambda] * len(optimizer.param_groups)
        else:
            if len(lr_lambda) != len(optimizer.param_groups):
                raise ValueError(
                    f"Expected {len(optimizer.param_groups)} lr_lambdas, but got {len(lr_lambda)}"
                )
            self.lr_lambdas = list(lr_lambda)
        super().__init__(optimizer, last_epoch)

    def state_dict(self):
        """Return the state of the scheduler as a :class:`dict`.

        It contains an entry for every variable in self.__dict__ which
        is not the optimizer.
        The learning rate lambda functions will only be saved if they are callable objects
        and not if they are functions or lambdas.

        When saving or loading the scheduler, please make sure to also save or load the state of the optimizer.
        """
        state_dict = {
            key: value
            for key, value in self.__dict__.items()
            if key not in ("optimizer", "lr_lambdas")
        }
        state_dict["lr_lambdas"] = [None] * len(self.lr_lambdas)

        for idx, fn in enumerate(self.lr_lambdas):
            if not isinstance(fn, types.FunctionType):
                state_dict["lr_lambdas"][idx] = fn.__dict__.copy()

        return state_dict

    def load_state_dict(self, state_dict):
        """Load the scheduler's state.

        When saving or loading the scheduler, please make sure to also save or load the state of the optimizer.

        Args:
            state_dict (dict): scheduler state. Should be an object returned
                from a call to :meth:`state_dict`.
        """
        lr_lambdas = state_dict.pop("lr_lambdas")
        self.__dict__.update(state_dict)
        # Restore state_dict keys in order to prevent side effects
        # https://github.com/pytorch/pytorch/issues/32756
        state_dict["lr_lambdas"] = lr_lambdas

        for idx, fn in enumerate(lr_lambdas):
            if fn is not None:
                self.lr_lambdas[idx].__dict__.update(fn)

    def get_lr(self):
        """Compute learning rate."""
        _warn_get_lr_called_within_step(self)

        return [
            base_lr * lmbda(self.last_epoch)
            for lmbda, base_lr in zip(self.lr_lambdas, self.base_lrs)
        ]


class MultiplicativeLR(LRScheduler):
    """Multiply the learning rate of each parameter group by the factor given in the specified function.

    When last_epoch=-1, set initial lr as lr.

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        lr_lambda (function or list): A function which computes a multiplicative
            factor given an integer parameter epoch, or a list of such
            functions, one for each group in optimizer.param_groups.
        last_epoch (int): The index of last epoch. Default: -1.

    Example:
        >>> # xdoctest: +SKIP
        >>> lmbda = lambda epoch: 0.95
        >>> scheduler = MultiplicativeLR(optimizer, lr_lambda=lmbda)
        >>> for epoch in range(100):
        >>>     train(...)
        >>>     validate(...)
        >>>     scheduler.step()
    """

    def __init__(
        self,
        optimizer: Optimizer,
        lr_lambda: Union[Callable[[int], float], list[Callable[[int], float]]],
        last_epoch: int = -1,
    ):  # noqa: D107
        self.optimizer = optimizer

        self.lr_lambdas: list[Callable[[int], float]]
        if not isinstance(lr_lambda, list) and not isinstance(lr_lambda, tuple):
            self.lr_lambdas = [lr_lambda] * len(optimizer.param_groups)
        else:
            if len(lr_lambda) != len(optimizer.param_groups):
                raise ValueError(
                    f"Expected {len(optimizer.param_groups)} lr_lambdas, but got {len(lr_lambda)}"
                )
            self.lr_lambdas = list(lr_lambda)
        super().__init__(optimizer, last_epoch)

    def state_dict(self):
        """Return the state of the scheduler as a :class:`dict`.

        It contains an entry for every variable in self.__dict__ which
        is not the optimizer.
        The learning rate lambda functions will only be saved if they are callable objects
        and not if they are functions or lambdas.
        """
        state_dict = {
            key: value
            for key, value in self.__dict__.items()
            if key not in ("optimizer", "lr_lambdas")
        }
        state_dict["lr_lambdas"] = [None] * len(self.lr_lambdas)

        for idx, fn in enumerate(self.lr_lambdas):
            if not isinstance(fn, types.FunctionType):
                state_dict["lr_lambdas"][idx] = fn.__dict__.copy()

        return state_dict

    def load_state_dict(self, state_dict):
        """Load the scheduler's state.

        Args:
            state_dict (dict): scheduler state. Should be an object returned
                from a call to :meth:`state_dict`.
        """
        lr_lambdas = state_dict.pop("lr_lambdas")
        self.__dict__.update(state_dict)
        # Restore state_dict keys in order to prevent side effects
        # https://github.com/pytorch/pytorch/issues/32756
        state_dict["lr_lambdas"] = lr_lambdas

        for idx, fn in enumerate(lr_lambdas):
            if fn is not None:
                self.lr_lambdas[idx].__dict__.update(fn)

    def get_lr(self):
        """Compute the learning rate of each parameter group."""
        _warn_get_lr_called_within_step(self)

        if self.last_epoch > 0:
            return [
                group["lr"] * lmbda(self.last_epoch)
                for lmbda, group in zip(self.lr_lambdas, self.optimizer.param_groups)
            ]
        else:
            return [group["lr"] for group in self.optimizer.param_groups]


class StepLR(LRScheduler):
    """Decays the learning rate of each parameter group by gamma every step_size epochs.

    Notice that such decay can happen simultaneously with other changes to the learning rate
    from outside this scheduler. When last_epoch=-1, sets initial lr as lr.

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        step_size (int): Period of learning rate decay.
        gamma (float): Multiplicative factor of learning rate decay.
            Default: 0.1.
        last_epoch (int): The index of last epoch. Default: -1.

    Example:
        >>> # xdoctest: +SKIP
        >>> # Assuming optimizer uses lr = 0.05 for all groups
        >>> # lr = 0.05     if epoch < 30
        >>> # lr = 0.005    if 30 <= epoch < 60
        >>> # lr = 0.0005   if 60 <= epoch < 90
        >>> # ...
        >>> scheduler = StepLR(optimizer, step_size=30, gamma=0.1)
        >>> for epoch in range(100):
        >>>     train(...)
        >>>     validate(...)
        >>>     scheduler.step()
    """

    def __init__(
        self,
        optimizer: Optimizer,
        step_size: int,
        gamma: float = 0.1,
        last_epoch: int = -1,
    ):  # noqa: D107
        self.step_size = step_size
        self.gamma = gamma
        super().__init__(optimizer, last_epoch)

    def get_lr(self):
        """Compute the learning rate of each parameter group."""
        _warn_get_lr_called_within_step(self)

        if (self.last_epoch == 0) or (self.last_epoch % self.step_size != 0):
            return [group["lr"] for group in self.optimizer.param_groups]
        return [group["lr"] * self.gamma for group in self.optimizer.param_groups]

    def _get_closed_form_lr(self):
        return [
            base_lr * self.gamma ** (self.last_epoch // self.step_size)
            for base_lr in self.base_lrs
        ]


class MultiStepLR(LRScheduler):
    """Decays the learning rate of each parameter group by gamma once the number of epoch reaches one of the milestones.

    Notice that such decay can happen simultaneously with other changes to the learning rate
    from outside this scheduler. When last_epoch=-1, sets initial lr as lr.

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        milestones (list): List of epoch indices. Must be increasing.
        gamma (float): Multiplicative factor of learning rate decay.
            Default: 0.1.
        last_epoch (int): The index of last epoch. Default: -1.

    Example:
        >>> # xdoctest: +SKIP
        >>> # Assuming optimizer uses lr = 0.05 for all groups
        >>> # lr = 0.05     if epoch < 30
        >>> # lr = 0.005    if 30 <= epoch < 80
        >>> # lr = 0.0005   if epoch >= 80
        >>> scheduler = MultiStepLR(optimizer, milestones=[30,80], gamma=0.1)
        >>> for epoch in range(100):
        >>>     train(...)
        >>>     validate(...)
        >>>     scheduler.step()
    """

    def __init__(
        self,
        optimizer: Optimizer,
        milestones: Iterable[int],
        gamma: float = 0.1,
        last_epoch: int = -1,
    ):  # noqa: D107
        self.milestones = Counter(milestones)
        self.gamma = gamma
        super().__init__(optimizer, last_epoch)

    def get_lr(self):
        """Compute the learning rate of each parameter group."""
        _warn_get_lr_called_within_step(self)

        if self.last_epoch not in self.milestones:
            return [group["lr"] for group in self.optimizer.param_groups]
        return [
            group["lr"] * self.gamma ** self.milestones[self.last_epoch]
            for group in self.optimizer.param_groups
        ]

    def _get_closed_form_lr(self):
        milestones = sorted(self.milestones.elements())
        return [
            base_lr * self.gamma ** bisect_right(milestones, self.last_epoch)
            for base_lr in self.base_lrs
        ]


class ConstantLR(LRScheduler):
    """Multiply the learning rate of each parameter group by a small constant factor.

    The multiplication is done until the number of epoch reaches a pre-defined milestone: total_iters.
    Notice that such multiplication of the small constant factor can
    happen simultaneously with other changes to the learning rate from outside this scheduler.
    When last_epoch=-1, sets initial lr as lr.

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        factor (float): The number we multiply learning rate until the milestone. Default: 1./3.
        total_iters (int): The number of steps that the scheduler multiplies the learning rate by the factor.
            Default: 5.
        last_epoch (int): The index of the last epoch. Default: -1.

    Example:
        >>> # xdoctest: +SKIP
        >>> # Assuming optimizer uses lr = 0.05 for all groups
        >>> # lr = 0.025   if epoch == 0
        >>> # lr = 0.025   if epoch == 1
        >>> # lr = 0.025   if epoch == 2
        >>> # lr = 0.025   if epoch == 3
        >>> # lr = 0.05    if epoch >= 4
        >>> scheduler = ConstantLR(optimizer, factor=0.5, total_iters=4)
        >>> for epoch in range(100):
        >>>     train(...)
        >>>     validate(...)
        >>>     scheduler.step()
    """

    def __init__(
        self,
        optimizer: Optimizer,
        factor: float = 1.0 / 3,
        total_iters: int = 5,
        last_epoch: int = -1,
    ):  # noqa: D107
        if factor > 1.0 or factor < 0:
            raise ValueError(
                "Constant multiplicative factor expected to be between 0 and 1."
            )

        self.factor = factor
        self.total_iters = total_iters
        super().__init__(optimizer, last_epoch)

    def get_lr(self):
        """Compute the learning rate of each parameter group."""
        _warn_get_lr_called_within_step(self)

        if self.last_epoch == 0:
            return [group["lr"] * self.factor for group in self.optimizer.param_groups]

        if self.last_epoch != self.total_iters:
            return [group["lr"] for group in self.optimizer.param_groups]

        return [
            group["lr"] * (1.0 / self.factor) for group in self.optimizer.param_groups
        ]

    def _get_closed_form_lr(self):
        return [
            base_lr
            * (self.factor + (self.last_epoch >= self.total_iters) * (1 - self.factor))
            for base_lr in self.base_lrs
        ]


class LinearLR(LRScheduler):
    """Decays the learning rate of each parameter group by linearly changing small multiplicative factor.

    The multiplication is done until the number of epoch reaches a pre-defined milestone: total_iters.
    Notice that such decay can happen simultaneously with other changes to the learning rate
    from outside this scheduler. When last_epoch=-1, sets initial lr as lr.

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        start_factor (float): The number we multiply learning rate in the first epoch.
            The multiplication factor changes towards end_factor in the following epochs.
            Default: 1./3.
        end_factor (float): The number we multiply learning rate at the end of linear changing
            process. Default: 1.0.
        total_iters (int): The number of iterations that multiplicative factor reaches to 1.
            Default: 5.
        last_epoch (int): The index of the last epoch. Default: -1.

    Example:
        >>> # xdoctest: +SKIP
        >>> # Assuming optimizer uses lr = 0.05 for all groups
        >>> # lr = 0.025    if epoch == 0
        >>> # lr = 0.03125  if epoch == 1
        >>> # lr = 0.0375   if epoch == 2
        >>> # lr = 0.04375  if epoch == 3
        >>> # lr = 0.05    if epoch >= 4
        >>> scheduler = LinearLR(optimizer, start_factor=0.5, total_iters=4)
        >>> for epoch in range(100):
        >>>     train(...)
        >>>     validate(...)
        >>>     scheduler.step()
    """

    def __init__(
        self,
        optimizer: Optimizer,
        start_factor: float = 1.0 / 3,
        end_factor: float = 1.0,
        total_iters: int = 5,
        last_epoch: int = -1,
    ):  # noqa: D107
        if start_factor > 1.0 or start_factor <= 0:
            raise ValueError(
                "Starting multiplicative factor expected to be greater than 0 and less or equal to 1."
            )

        if end_factor > 1.0 or end_factor < 0:
            raise ValueError(
                "Ending multiplicative factor expected to be between 0 and 1."
            )

        self.start_factor = start_factor
        self.end_factor = end_factor
        self.total_iters = total_iters
        super().__init__(optimizer, last_epoch)

    def get_lr(self):
        """Compute the learning rate."""
        _warn_get_lr_called_within_step(self)

        if self.last_epoch == 0:
            return [
                group["lr"] * self.start_factor for group in self.optimizer.param_groups
            ]

        if self.last_epoch > self.total_iters:
            return [group["lr"] for group in self.optimizer.param_groups]

        return [
            group["lr"]
            * (
                1.0
                + (self.end_factor - self.start_factor)
                / (
                    self.total_iters * self.start_factor
                    + (self.last_epoch - 1) * (self.end_factor - self.start_factor)
                )
            )
            for group in self.optimizer.param_groups
        ]

    def _get_closed_form_lr(self):
        return [
            base_lr
            * (
                self.start_factor
                + (self.end_factor - self.start_factor)
                * min(self.total_iters, self.last_epoch)
                / self.total_iters
            )
            for base_lr in self.base_lrs
        ]


class ExponentialLR(LRScheduler):
    """Decays the learning rate of each parameter group by gamma every epoch.

    When last_epoch=-1, sets initial lr as lr.

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        gamma (float): Multiplicative factor of learning rate decay.
        last_epoch (int): The index of last epoch. Default: -1.
    """

    def __init__(
        self,
        optimizer: Optimizer,
        gamma: float,
        last_epoch: int = -1,
    ):  # noqa: D107
        self.gamma = gamma
        super().__init__(optimizer, last_epoch)

    def get_lr(self):
        """Compute the learning rate of each parameter group."""
        _warn_get_lr_called_within_step(self)

        if self.last_epoch == 0:
            return [group["lr"] for group in self.optimizer.param_groups]
        return [group["lr"] * self.gamma for group in self.optimizer.param_groups]

    def _get_closed_form_lr(self):
        return [base_lr * self.gamma**self.last_epoch for base_lr in self.base_lrs]


class SequentialLR(LRScheduler):
    """Contains a list of schedulers expected to be called sequentially during the optimization process.

    Specifically, the schedulers will be called according to the milestone points, which should provide exact
    intervals by which each scheduler should be called at a given epoch.

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        schedulers (list): List of chained schedulers.
        milestones (list): List of integers that reflects milestone points.
        last_epoch (int): The index of last epoch. Default: -1.

    Example:
        >>> # xdoctest: +SKIP
        >>> # Assuming optimizer uses lr = 1. for all groups
        >>> # lr = 0.1     if epoch == 0
        >>> # lr = 0.1     if epoch == 1
        >>> # lr = 0.9     if epoch == 2
        >>> # lr = 0.81    if epoch == 3
        >>> # lr = 0.729   if epoch == 4
        >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=2)
        >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9)
        >>> scheduler = SequentialLR(optimizer, schedulers=[scheduler1, scheduler2], milestones=[2])
        >>> for epoch in range(100):
        >>>     train(...)
        >>>     validate(...)
        >>>     scheduler.step()
    """

    def __init__(
        self,
        optimizer: Optimizer,
        schedulers: list[LRScheduler],
        milestones: list[int],
        last_epoch: int = -1,
    ):  # noqa: D107
        if len(schedulers) < 1:
            raise ValueError(
                f"{self.__class__.__name__} expects at least one scheduler, but got no scheduler."
            )

        for scheduler_idx, scheduler in enumerate(schedulers):
            if not hasattr(scheduler, "optimizer"):
                raise TypeError(
                    f"{self.__class__.__name__} at index {scheduler_idx} should have `optimizer` as its attribute."
                )
            if isinstance(scheduler, ReduceLROnPlateau):
                raise ValueError(
                    f"{self.__class__.__name__} does not support `ReduceLROnPlateau` scheduler as it "
                    "requires additional kwargs to be specified when calling `step`, "
                    f"but got one at index {scheduler_idx} in the given schedulers sequence."
                )
            if optimizer != scheduler.optimizer:
                raise ValueError(
                    f"{self.__class__.__name__} expects all schedulers to belong to the same optimizer, but "
                    f"got scheduler {scheduler.__class__.__name__} at index {scheduler_idx} has {scheduler.optimizer}, "
                    f"which is different from {optimizer.__class__.__name__}."
                )

        if len(milestones) != len(schedulers) - 1:
            raise ValueError(
                "Sequential Schedulers expects number of schedulers provided to be one more "
                f"than the number of milestone points, but got number of schedulers {len(schedulers)} and the "
                f"number of milestones to be equal to {len(milestones)}"
            )
        self._schedulers = schedulers
        self._milestones = milestones
        self.last_epoch = last_epoch + 1
        self.optimizer = optimizer

        # Reset learning rates back to initial values
        for group in self.optimizer.param_groups:
            group["lr"] = group["initial_lr"]

        # "Undo" the step performed by other schedulers
        self.recursive_undo()

        # Perform the initial step for only the first scheduler
        self._schedulers[0]._initial_step()

        self._last_lr = schedulers[0].get_last_lr()

    def recursive_undo(self, sched=None):
        """
        Recursively undo any step performed by the initialisation of
        schedulers.
        """
        scheds = self if sched is None else sched

        if hasattr(scheds, "_schedulers"):
            for s in scheds._schedulers:
                self.recursive_undo(s)
        elif hasattr(scheds, "last_epoch"):
            scheds.last_epoch -= 1

    def step(self):  # type: ignore[override]
        """Perform a step."""
        self.last_epoch += 1
        idx = bisect_right(self._milestones, self.last_epoch)
        scheduler = self._schedulers[idx]
        if idx > 0 and self._milestones[idx - 1] == self.last_epoch:
            scheduler.step(0)
        else:
            scheduler.step()

        self._last_lr = scheduler.get_last_lr()

    def state_dict(self):
        """Return the state of the scheduler as a :class:`dict`.

        It contains an entry for every variable in self.__dict__ which
        is not the optimizer.
        The wrapped scheduler states will also be saved.
        """
        state_dict = {
            key: value
            for key, value in self.__dict__.items()
            if key not in ("optimizer", "_schedulers")
        }
        state_dict["_schedulers"] = [None] * len(self._schedulers)

        for idx, s in enumerate(self._schedulers):
            state_dict["_schedulers"][idx] = s.state_dict()

        return state_dict

    def load_state_dict(self, state_dict):
        """Load the scheduler's state.

        Args:
            state_dict (dict): scheduler state. Should be an object returned
                from a call to :meth:`state_dict`.
        """
        _schedulers = state_dict.pop("_schedulers")
        self.__dict__.update(state_dict)
        # Restore state_dict keys in order to prevent side effects
        # https://github.com/pytorch/pytorch/issues/32756
        state_dict["_schedulers"] = _schedulers

        for idx, s in enumerate(_schedulers):
            self._schedulers[idx].load_state_dict(s)


class PolynomialLR(LRScheduler):
    """Decays the learning rate of each parameter group using a polynomial function in the given total_iters.

    When last_epoch=-1, sets initial lr as lr.

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        total_iters (int): The number of steps that the scheduler decays the learning rate. Default: 5.
        power (float): The power of the polynomial. Default: 1.0.

    Example:
        >>> # xdoctest: +SKIP("undefined vars")
        >>> # Assuming optimizer uses lr = 0.001 for all groups
        >>> # lr = 0.001     if epoch == 0
        >>> # lr = 0.00075   if epoch == 1
        >>> # lr = 0.00050   if epoch == 2
        >>> # lr = 0.00025   if epoch == 3
        >>> # lr = 0.0       if epoch >= 4
        >>> scheduler = PolynomialLR(optimizer, total_iters=4, power=1.0)
        >>> for epoch in range(100):
        >>>     train(...)
        >>>     validate(...)
        >>>     scheduler.step()
    """

    def __init__(
        self,
        optimizer: Optimizer,
        total_iters: int = 5,
        power: float = 1.0,
        last_epoch: int = -1,
    ):  # noqa: D107
        self.total_iters = total_iters
        self.power = power
        super().__init__(optimizer, last_epoch)

    def get_lr(self):
        """Compute the learning rate."""
        _warn_get_lr_called_within_step(self)

        if self.last_epoch == 0 or self.last_epoch > self.total_iters:
            return [group["lr"] for group in self.optimizer.param_groups]

        decay_factor = (
            (1.0 - self.last_epoch / self.total_iters)
            / (1.0 - (self.last_epoch - 1) / self.total_iters)
        ) ** self.power
        return [group["lr"] * decay_factor for group in self.optimizer.param_groups]

    def _get_closed_form_lr(self):
        return [
            (
                base_lr
                * (1.0 - min(self.total_iters, self.last_epoch) / self.total_iters)
                ** self.power
            )
            for base_lr in self.base_lrs
        ]


class CosineAnnealingLR(LRScheduler):
    r"""Set the learning rate of each parameter group using a cosine annealing schedule.

    The :math:`\eta_{max}` is set to the initial lr and
    :math:`T_{cur}` is the number of epochs since the last restart in SGDR:

    .. math::
        \begin{aligned}
            \eta_t & = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1
            + \cos\left(\frac{T_{cur}}{T_{max}}\pi\right)\right),
            & T_{cur} \neq (2k+1)T_{max}; \\
            \eta_{t+1} & = \eta_{t} + \frac{1}{2}(\eta_{max} - \eta_{min})
            \left(1 - \cos\left(\frac{1}{T_{max}}\pi\right)\right),
            & T_{cur} = (2k+1)T_{max}.
        \end{aligned}

    When last_epoch=-1, sets initial lr as lr. Notice that because the schedule
    is defined recursively, the learning rate can be simultaneously modified
    outside this scheduler by other operators. If the learning rate is set
    solely by this scheduler, the learning rate at each step becomes:

    .. math::
        \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 +
        \cos\left(\frac{T_{cur}}{T_{max}}\pi\right)\right)

    It has been proposed in
    `SGDR: Stochastic Gradient Descent with Warm Restarts`_. Note that this only
    implements the cosine annealing part of SGDR, and not the restarts.

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        T_max (int): Maximum number of iterations.
        eta_min (float): Minimum learning rate. Default: 0.
        last_epoch (int): The index of last epoch. Default: -1.

    .. _SGDR\: Stochastic Gradient Descent with Warm Restarts:
        https://arxiv.org/abs/1608.03983
    """

    def __init__(
        self,
        optimizer: Optimizer,
        T_max: int,
        eta_min: float = 0.0,
        last_epoch: int = -1,
    ):  # noqa: D107
        self.T_max = T_max
        self.eta_min = eta_min
        super().__init__(optimizer, last_epoch)

    def get_lr(self):
        """Retrieve the learning rate of each parameter group."""
        _warn_get_lr_called_within_step(self)

        if self.last_epoch == 0:
            return [group["lr"] for group in self.optimizer.param_groups]
        elif self._step_count == 1 and self.last_epoch > 0:
            return [
                self.eta_min
                + (base_lr - self.eta_min)
                * (1 + math.cos((self.last_epoch) * math.pi / self.T_max))
                / 2
                for base_lr, group in zip(self.base_lrs, self.optimizer.param_groups)
            ]
        elif (self.last_epoch - 1 - self.T_max) % (2 * self.T_max) == 0:
            return [
                group["lr"]
                + (base_lr - self.eta_min) * (1 - math.cos(math.pi / self.T_max)) / 2
                for base_lr, group in zip(self.base_lrs, self.optimizer.param_groups)
            ]
        return [
            (1 + math.cos(math.pi * self.last_epoch / self.T_max))
            / (1 + math.cos(math.pi * (self.last_epoch - 1) / self.T_max))
            * (group["lr"] - self.eta_min)
            + self.eta_min
            for group in self.optimizer.param_groups
        ]

    def _get_closed_form_lr(self):
        return [
            self.eta_min
            + (base_lr - self.eta_min)
            * (1 + math.cos(math.pi * self.last_epoch / self.T_max))
            / 2
            for base_lr in self.base_lrs
        ]


class ChainedScheduler(LRScheduler):
    """Chains a list of learning rate schedulers.

    Takes in a sequence of chainable learning rate schedulers and calls their
    step() functions consecutively in just one call to step().

    Args:
        schedulers (sequence): sequence of chained schedulers.
        optimizer (Optimizer, optional): Wrapped optimizer. Default: None.

    Example:
        >>> # xdoctest: +SKIP
        >>> # Assuming optimizer uses lr = 1. for all groups
        >>> # lr = 0.09     if epoch == 0
        >>> # lr = 0.081    if epoch == 1
        >>> # lr = 0.729    if epoch == 2
        >>> # lr = 0.6561   if epoch == 3
        >>> # lr = 0.59049  if epoch >= 4
        >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=2)
        >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9)
        >>> scheduler = ChainedScheduler([scheduler1, scheduler2], optimizer=optimizer)
        >>> for epoch in range(100):
        >>>     train(...)
        >>>     validate(...)
        >>>     scheduler.step()
    """

    def __init__(
        self, schedulers: Sequence[LRScheduler], optimizer: Optional[Optimizer] = None
    ):  # noqa: D107
        if len(schedulers) < 1:
            raise ValueError(
                f"{self.__class__.__name__} expects at least one scheduler to be chained, but got no scheduler."
            )

        optimizer = optimizer or schedulers[0].optimizer
        for scheduler_idx, scheduler in enumerate(schedulers):
            if not hasattr(scheduler, "optimizer"):
                raise TypeError(
                    f"{self.__class__.__name__} at index {scheduler_idx} should have `optimizer` as its attribute."
                )
            if isinstance(scheduler, ReduceLROnPlateau):
                raise ValueError(
                    f"{self.__class__.__name__} does not support `ReduceLROnPlateau` scheduler as it "
                    "requires additional kwargs to be specified when calling `step`, "
                    f"but got one at index {scheduler_idx} in the given schedulers sequence."
                )
            if optimizer != scheduler.optimizer:
                raise ValueError(
                    f"{self.__class__.__name__} expects all schedulers to belong to the same optimizer, but "
                    f"got scheduler {scheduler.__class__.__name__} at index {scheduler_idx} has {scheduler.optimizer}, "
                    f"which is different from {optimizer.__class__.__name__}."
                )
        self._schedulers = schedulers
        self.optimizer = optimizer
        self._last_lr = [
            group["lr"] for group in self._schedulers[-1].optimizer.param_groups
        ]

    def step(self):  # type: ignore[override]
        """Perform a step."""
        for scheduler in self._schedulers:
            scheduler.step()
        self._last_lr = [
            group["lr"] for group in self._schedulers[-1].optimizer.param_groups
        ]

    def state_dict(self):
        """Return the state of the scheduler as a :class:`dict`.

        It contains an entry for every variable in self.__dict__ which
        is not the optimizer.
        The wrapped scheduler states will also be saved.
        """
        state_dict = {
            key: value
            for key, value in self.__dict__.items()
            if key not in ("optimizer", "_schedulers")
        }
        state_dict["_schedulers"] = [None] * len(self._schedulers)

        for idx, s in enumerate(self._schedulers):
            state_dict["_schedulers"][idx] = s.state_dict()

        return state_dict

    def load_state_dict(self, state_dict):
        """Load the scheduler's state.

        Args:
            state_dict (dict): scheduler state. Should be an object returned
                from a call to :meth:`state_dict`.
        """
        _schedulers = state_dict.pop("_schedulers")
        self.__dict__.update(state_dict)
        # Restore state_dict keys in order to prevent side effects
        # https://github.com/pytorch/pytorch/issues/32756
        state_dict["_schedulers"] = _schedulers

        for idx, s in enumerate(_schedulers):
            self._schedulers[idx].load_state_dict(s)


class ReduceLROnPlateau(LRScheduler):
    """Reduce learning rate when a metric has stopped improving.

    Models often benefit from reducing the learning rate by a factor
    of 2-10 once learning stagnates. This scheduler reads a metrics
    quantity and if no improvement is seen for a 'patience' number
    of epochs, the learning rate is reduced.

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        mode (str): One of `min`, `max`. In `min` mode, lr will
            be reduced when the quantity monitored has stopped
            decreasing; in `max` mode it will be reduced when the
            quantity monitored has stopped increasing. Default: 'min'.
        factor (float): Factor by which the learning rate will be
            reduced. new_lr = lr * factor. Default: 0.1.
        patience (int): The number of allowed epochs with no improvement after
            which the learning rate will be reduced.
            For example, consider the case of having no patience (`patience = 0`).
            In the first epoch, a baseline is established and is always considered good as there's no previous baseline.
            In the second epoch, if the performance is worse than the baseline,
            we have what is considered an intolerable epoch.
            Since the count of intolerable epochs (1) is greater than the patience level (0),
            the learning rate is reduced at the end of this epoch.
            From the third epoch onwards, the learning rate continues to be reduced at the end of each epoch
            if the performance is worse than the baseline. If the performance improves or remains the same,
            the learning rate is not adjusted.
            Default: 10.
        threshold (float): Threshold for measuring the new optimum,
            to only focus on significant changes. Default: 1e-4.
        threshold_mode (str): One of `rel`, `abs`. In `rel` mode,
            dynamic_threshold = best * ( 1 + threshold ) in 'max'
            mode or best * ( 1 - threshold ) in `min` mode.
            In `abs` mode, dynamic_threshold = best + threshold in
            `max` mode or best - threshold in `min` mode. Default: 'rel'.
        cooldown (int): Number of epochs to wait before resuming
            normal operation after lr has been reduced. Default: 0.
        min_lr (float or list): A scalar or a list of scalars. A
            lower bound on the learning rate of all param groups
            or each group respectively. Default: 0.
        eps (float): Minimal decay applied to lr. If the difference
            between new and old lr is smaller than eps, the update is
            ignored. Default: 1e-8.

    Example:
        >>> # xdoctest: +SKIP
        >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
        >>> scheduler = ReduceLROnPlateau(optimizer, 'min')
        >>> for epoch in range(10):
        >>>     train(...)
        >>>     val_loss = validate(...)
        >>>     # Note that step should be called after validate()
        >>>     scheduler.step(val_loss)
    """

    def __init__(
        self,
        optimizer: Optimizer,
        mode: Literal["min", "max"] = "min",
        factor: float = 0.1,
        patience: int = 10,
        threshold: float = 1e-4,
        threshold_mode: Literal["rel", "abs"] = "rel",
        cooldown: int = 0,
        min_lr: Union[list[float], float] = 0,
        eps: float = 1e-8,
    ):  # noqa: D107
        if factor >= 1.0:
            raise ValueError("Factor should be < 1.0.")
        self.factor = factor

        # Attach optimizer
        if not isinstance(optimizer, Optimizer):
            raise TypeError(f"{type(optimizer).__name__} is not an Optimizer")
        self.optimizer = optimizer

        if isinstance(min_lr, (list, tuple)):
            if len(min_lr) != len(optimizer.param_groups):
                raise ValueError(
                    f"expected {len(optimizer.param_groups)} min_lrs, got {len(min_lr)}"
                )
            self.default_min_lr = None
            self.min_lrs = list(min_lr)
        else:
            self.default_min_lr = min_lr
            self.min_lrs = [min_lr] * len(optimizer.param_groups)

        self.patience = patience

        self.cooldown = cooldown
        self.cooldown_counter = 0
        self.mode = mode
        self.threshold = threshold
        self.threshold_mode = threshold_mode
        self.best: float
        self.num_bad_epochs: int
        self.mode_worse: float  # the worse value for the chosen mode
        self.eps = eps
        self.last_epoch = 0
        self._last_lr = [group["lr"] for group in self.optimizer.param_groups]
        self._init_is_better(
            mode=mode, threshold=threshold, threshold_mode=threshold_mode
        )
        self._reset()

    def _reset(self):
        """Reset num_bad_epochs counter and cooldown counter."""
        self.best = self.mode_worse
        self.cooldown_counter = 0
        self.num_bad_epochs = 0

    def step(self, metrics: SupportsFloat, epoch=None):  # type: ignore[override]
        """Perform a step."""
        # convert `metrics` to float, in case it's a zero-dim Tensor
        current = float(metrics)
        if epoch is None:
            epoch = self.last_epoch + 1
        else:
            warnings.warn(EPOCH_DEPRECATION_WARNING, UserWarning)
        self.last_epoch = epoch

        if self.is_better(current, self.best):
            self.best = current
            self.num_bad_epochs = 0
        else:
            self.num_bad_epochs += 1

        if self.in_cooldown:
            self.cooldown_counter -= 1
            self.num_bad_epochs = 0  # ignore any bad epochs in cooldown

        if self.num_bad_epochs > self.patience:
            self._reduce_lr(epoch)
            self.cooldown_counter = self.cooldown
            self.num_bad_epochs = 0

        self._last_lr = [group["lr"] for group in self.optimizer.param_groups]

    def _reduce_lr(self, epoch):
        if len(self.optimizer.param_groups) != len(self.min_lrs):
            if self.default_min_lr is None:
                raise RuntimeError(
                    "The number of param groups in the `optimizer` "
                    f"({len(self.optimizer.param_groups)}) differs "
                    f"from when `ReduceLROnPlateau` was initialized "
                    f"({len(self.min_lrs)}), usually due to a new "
                    "param group being added to the optimizer. Please "
                    "modify the `min_lrs` field to match the length "
                    "of the `optimizer` param groups."
                )
            else:
                self.min_lrs = [self.default_min_lr] * len(self.optimizer.param_groups)

        for i, param_group in enumerate(self.optimizer.param_groups):
            old_lr = float(param_group["lr"])
            new_lr = max(old_lr * self.factor, self.min_lrs[i])
            if old_lr - new_lr > self.eps:
                param_group["lr"] = new_lr

    @property
    def in_cooldown(self):  # noqa: D102
        return self.cooldown_counter > 0

    def is_better(self, a, best):  # noqa: D102
        if self.mode == "min" and self.threshold_mode == "rel":
            rel_epsilon = 1.0 - self.threshold
            return a < best * rel_epsilon

        elif self.mode == "min" and self.threshold_mode == "abs":
            return a < best - self.threshold

        elif self.mode == "max" and self.threshold_mode == "rel":
            rel_epsilon = self.threshold + 1.0
            return a > best * rel_epsilon

        else:  # mode == 'max' and epsilon_mode == 'abs':
            return a > best + self.threshold

    def _init_is_better(self, mode, threshold, threshold_mode):
        if mode not in {"min", "max"}:
            raise ValueError("mode " + mode + " is unknown!")
        if threshold_mode not in {"rel", "abs"}:
            raise ValueError("threshold mode " + threshold_mode + " is unknown!")

        if mode == "min":
            self.mode_worse = inf
        else:  # mode == 'max':
            self.mode_worse = -inf

        self.mode = mode
        self.threshold = threshold
        self.threshold_mode = threshold_mode

    def state_dict(self):  # noqa: D102
        return {
            key: value for key, value in self.__dict__.items() if key != "optimizer"
        }

    def load_state_dict(self, state_dict):
        """Load the scheduler's state."""
        self.__dict__.update(state_dict)
        self._init_is_better(
            mode=self.mode, threshold=self.threshold, threshold_mode=self.threshold_mode
        )


class CyclicLR(LRScheduler):
    r"""Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR).

    The policy cycles the learning rate between two boundaries with a constant frequency,
    as detailed in the paper `Cyclical Learning Rates for Training Neural Networks`_.
    The distance between the two boundaries can be scaled on a per-iteration
    or per-cycle basis.

    Cyclical learning rate policy changes the learning rate after every batch.
    `step` should be called after a batch has been used for training.

    This class has three built-in policies, as put forth in the paper:

    * "triangular": A basic triangular cycle without amplitude scaling.
    * "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle.
    * "exp_range": A cycle that scales initial amplitude by :math:`\text{gamma}^{\text{cycle iterations}}`
      at each cycle iteration.

    This implementation was adapted from the github repo: `bckenstler/CLR`_

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        base_lr (float or list): Initial learning rate which is the
            lower boundary in the cycle for each parameter group.
        max_lr (float or list): Upper learning rate boundaries in the cycle
            for each parameter group. Functionally,
            it defines the cycle amplitude (max_lr - base_lr).
            The lr at any cycle is the sum of base_lr
            and some scaling of the amplitude; therefore
            max_lr may not actually be reached depending on
            scaling function.
        step_size_up (int): Number of training iterations in the
            increasing half of a cycle. Default: 2000
        step_size_down (int): Number of training iterations in the
            decreasing half of a cycle. If step_size_down is None,
            it is set to step_size_up. Default: None
        mode (str): One of {triangular, triangular2, exp_range}.
            Values correspond to policies detailed above.
            If scale_fn is not None, this argument is ignored.
            Default: 'triangular'
        gamma (float): Constant in 'exp_range' scaling function:
            gamma**(cycle iterations)
            Default: 1.0
        scale_fn (function): Custom scaling policy defined by a single
            argument lambda function, where
            0 <= scale_fn(x) <= 1 for all x >= 0.
            If specified, then 'mode' is ignored.
            Default: None
        scale_mode (str): {'cycle', 'iterations'}.
            Defines whether scale_fn is evaluated on
            cycle number or cycle iterations (training
            iterations since start of cycle).
            Default: 'cycle'
        cycle_momentum (bool): If ``True``, momentum is cycled inversely
            to learning rate between 'base_momentum' and 'max_momentum'.
            Default: True
        base_momentum (float or list): Lower momentum boundaries in the cycle
            for each parameter group. Note that momentum is cycled inversely
            to learning rate; at the peak of a cycle, momentum is
            'base_momentum' and learning rate is 'max_lr'.
            Default: 0.8
        max_momentum (float or list): Upper momentum boundaries in the cycle
            for each parameter group. Functionally,
            it defines the cycle amplitude (max_momentum - base_momentum).
            The momentum at any cycle is the difference of max_momentum
            and some scaling of the amplitude; therefore
            base_momentum may not actually be reached depending on
            scaling function. Note that momentum is cycled inversely
            to learning rate; at the start of a cycle, momentum is 'max_momentum'
            and learning rate is 'base_lr'
            Default: 0.9
        last_epoch (int): The index of the last batch. This parameter is used when
            resuming a training job. Since `step()` should be invoked after each
            batch instead of after each epoch, this number represents the total
            number of *batches* computed, not the total number of epochs computed.
            When last_epoch=-1, the schedule is started from the beginning.
            Default: -1

    Example:
        >>> # xdoctest: +SKIP
        >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
        >>> scheduler = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.01, max_lr=0.1)
        >>> data_loader = torch.utils.data.DataLoader(...)
        >>> for epoch in range(10):
        >>>     for batch in data_loader:
        >>>         train_batch(...)
        >>>         scheduler.step()


    .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186
    .. _bckenstler/CLR: https://github.com/bckenstler/CLR
    """

    def __init__(
        self,
        optimizer: Optimizer,
        base_lr: Union[float, list[float]],
        max_lr: Union[float, list[float]],
        step_size_up: int = 2000,
        step_size_down: Optional[int] = None,
        mode: Literal["triangular", "triangular2", "exp_range"] = "triangular",
        gamma: float = 1.0,
        scale_fn: Optional[Callable[[float], float]] = None,
        scale_mode: Literal["cycle", "iterations"] = "cycle",
        cycle_momentum: bool = True,
        base_momentum: float = 0.8,
        max_momentum: float = 0.9,
        last_epoch: int = -1,
    ):  # noqa: D107
        # Attach optimizer
        if not isinstance(optimizer, Optimizer):
            raise TypeError(f"{type(optimizer).__name__} is not an Optimizer")
        self.optimizer = optimizer

        base_lrs = _format_param("base_lr", optimizer, base_lr)
        if last_epoch == -1:
            for lr, group in zip(base_lrs, optimizer.param_groups):
                if isinstance(group["lr"], Tensor):
                    lr_val = lr.item() if isinstance(lr, Tensor) else lr
                    group["lr"].fill_(lr_val)
                else:
                    group["lr"] = lr

        self.max_lrs = _format_param("max_lr", optimizer, max_lr)

        step_size_up = float(step_size_up)
        step_size_down = (
            float(step_size_down) if step_size_down is not None else step_size_up
        )
        self.total_size = step_size_up + step_size_down
        self.step_ratio = step_size_up / self.total_size

        if mode not in ["triangular", "triangular2", "exp_range"] and scale_fn is None:
            raise ValueError("mode is invalid and scale_fn is None")

        self.mode = mode
        self.gamma = gamma

        self._scale_fn_ref: Callable[[float], float]
        self._scale_fn_custom = scale_fn
        self.scale_mode = scale_mode
        self._init_scale_fn()

        self.cycle_momentum = cycle_momentum
        if cycle_momentum:
            if (
                "momentum" not in optimizer.defaults
                and "betas" not in optimizer.defaults
            ):
                raise ValueError(
                    "optimizer must support momentum or beta1 with `cycle_momentum` option enabled"
                )

            self.use_beta1 = "betas" in self.optimizer.defaults
            self.base_momentums = _format_param(
                "base_momentum", optimizer, base_momentum
            )
            self.max_momentums = _format_param("max_momentum", optimizer, max_momentum)
            if last_epoch == -1:
                for m_momentum, b_momentum, group in zip(
                    self.max_momentums, self.base_momentums, optimizer.param_groups
                ):
                    if self.use_beta1:
                        group["betas"] = (m_momentum, *group["betas"][1:])
                    else:
                        group["momentum"] = m_momentum
                    group["max_momentum"] = m_momentum
                    group["base_momentum"] = b_momentum

        super().__init__(optimizer, last_epoch)
        self.base_lrs = base_lrs

    def _init_scale_fn(self):
        if self._scale_fn_custom is not None:
            return
        if self.mode == "triangular":
            self._scale_fn_ref = self._triangular_scale_fn
            self.scale_mode = "cycle"
        elif self.mode == "triangular2":
            self._scale_fn_ref = self._triangular2_scale_fn
            self.scale_mode = "cycle"
        elif self.mode == "exp_range":
            self._scale_fn_ref = partial(self._exp_range_scale_fn, self.gamma)
            self.scale_mode = "iterations"

    def scale_fn(self, x) -> float:
        """Get the scaling policy."""
        if self._scale_fn_custom is not None:
            return self._scale_fn_custom(x)
        else:
            return self._scale_fn_ref(x)  # static method

    @staticmethod
    def _triangular_scale_fn(x: float) -> float:
        return 1.0

    @staticmethod
    def _triangular2_scale_fn(x: float) -> float:
        return 1 / (2.0 ** (x - 1))

    @staticmethod
    def _exp_range_scale_fn(gamma: float, x: float) -> float:
        return gamma**x

    def get_lr(self):
        """Calculate the learning rate at batch index.

        This function treats `self.last_epoch` as the last batch index.

        If `self.cycle_momentum` is ``True``, this function has a side effect of
        updating the optimizer's momentum.
        """
        _warn_get_lr_called_within_step(self)

        cycle = math.floor(1 + self.last_epoch / self.total_size)
        x = 1.0 + self.last_epoch / self.total_size - cycle
        if x <= self.step_ratio:
            scale_factor = x / self.step_ratio
        else:
            scale_factor = (x - 1) / (self.step_ratio - 1)

        lrs = []
        for base_lr, max_lr in zip(self.base_lrs, self.max_lrs):
            base_height = (max_lr - base_lr) * scale_factor
            if self.scale_mode == "cycle":
                lr = base_lr + base_height * self.scale_fn(cycle)
            else:
                lr = base_lr + base_height * self.scale_fn(self.last_epoch)
            lrs.append(lr)

        if self.cycle_momentum:
            momentums = []
            for base_momentum, max_momentum in zip(
                self.base_momentums, self.max_momentums
            ):
                base_height = (max_momentum - base_momentum) * scale_factor
                if self.scale_mode == "cycle":
                    momentum = max_momentum - base_height * self.scale_fn(cycle)
                else:
                    momentum = max_momentum - base_height * self.scale_fn(
                        self.last_epoch
                    )
                momentums.append(momentum)
            for param_group, momentum in zip(self.optimizer.param_groups, momentums):
                if self.use_beta1:
                    param_group["betas"] = (momentum, *param_group["betas"][1:])
                else:
                    param_group["momentum"] = momentum

        return lrs

    def state_dict(self):  # noqa: D102
        state = super().state_dict()
        # We are dropping the `_scale_fn_ref` attribute because it is a
        # `weakref.WeakMethod` and can't be pickled.
        state.pop("_scale_fn_ref", None)
        fn = state.pop("_scale_fn_custom")
        state["_scale_fn_custom"] = None
        if fn is not None and not isinstance(fn, types.FunctionType):
            # The _scale_fn_custom will only be saved if it is a callable object
            # and not if it is a function or lambda.
            state["_scale_fn_custom"] = fn.__dict__.copy()

        return state

    def load_state_dict(self, state_dict):
        """Load the scheduler's state."""
        fn = state_dict.pop("_scale_fn_custom")
        super().load_state_dict(state_dict)
        if fn is not None:
            self._scale_fn_custom.__dict__.update(fn)
        self._init_scale_fn()


class CosineAnnealingWarmRestarts(LRScheduler):
    r"""Set the learning rate of each parameter group using a cosine annealing schedule.

    The :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}`
    is the number of epochs since the last restart and :math:`T_{i}` is the number
    of epochs between two warm restarts in SGDR:

    .. math::
        \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 +
        \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right)

    When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`.
    When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`.

    It has been proposed in
    `SGDR: Stochastic Gradient Descent with Warm Restarts`_.

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        T_0 (int): Number of iterations until the first restart.
        T_mult (int, optional): A factor by which :math:`T_{i}` increases after a restart. Default: 1.
        eta_min (float, optional): Minimum learning rate. Default: 0.
        last_epoch (int, optional): The index of the last epoch. Default: -1.

    .. _SGDR\: Stochastic Gradient Descent with Warm Restarts:
        https://arxiv.org/abs/1608.03983
    """

    def __init__(
        self,
        optimizer: Optimizer,
        T_0: int,
        T_mult: int = 1,
        eta_min: float = 0.0,
        last_epoch: int = -1,
    ):  # noqa: D107
        if T_0 <= 0 or not isinstance(T_0, int):
            raise ValueError(f"Expected positive integer T_0, but got {T_0}")
        if T_mult < 1 or not isinstance(T_mult, int):
            raise ValueError(f"Expected integer T_mult >= 1, but got {T_mult}")
        if not isinstance(eta_min, (float, int)):
            raise ValueError(
                f"Expected float or int eta_min, but got {eta_min} of type {type(eta_min)}"
            )
        self.T_0 = T_0
        self.T_i = T_0
        self.T_mult = T_mult
        self.eta_min = eta_min
        self.T_cur = last_epoch
        super().__init__(optimizer, last_epoch)

    def get_lr(self):
        """Compute the initial learning rate."""
        _warn_get_lr_called_within_step(self)

        return [
            self.eta_min
            + (base_lr - self.eta_min)
            * (1 + math.cos(math.pi * self.T_cur / self.T_i))
            / 2
            for base_lr in self.base_lrs
        ]

    def step(self, epoch=None):
        """Step could be called after every batch update.

        Example:
            >>> # xdoctest: +SKIP("Undefined vars")
            >>> scheduler = CosineAnnealingWarmRestarts(optimizer, T_0, T_mult)
            >>> iters = len(dataloader)
            >>> for epoch in range(20):
            >>>     for i, sample in enumerate(dataloader):
            >>>         inputs, labels = sample['inputs'], sample['labels']
            >>>         optimizer.zero_grad()
            >>>         outputs = net(inputs)
            >>>         loss = criterion(outputs, labels)
            >>>         loss.backward()
            >>>         optimizer.step()
            >>>         scheduler.step(epoch + i / iters)

        This function can be called in an interleaved way.

        Example:
            >>> # xdoctest: +SKIP("Undefined vars")
            >>> scheduler = CosineAnnealingWarmRestarts(optimizer, T_0, T_mult)
            >>> for epoch in range(20):
            >>>     scheduler.step()
            >>> scheduler.step(26)
            >>> scheduler.step() # scheduler.step(27), instead of scheduler(20)
        """
        if epoch is None and self.last_epoch < 0:
            epoch = 0

        if epoch is None:
            epoch = self.last_epoch + 1
            self.T_cur = self.T_cur + 1
            if self.T_cur >= self.T_i:
                self.T_cur = self.T_cur - self.T_i
                self.T_i = self.T_i * self.T_mult
        else:
            if epoch < 0:
                raise ValueError(f"Expected non-negative epoch, but got {epoch}")
            if epoch >= self.T_0:
                if self.T_mult == 1:
                    self.T_cur = epoch % self.T_0
                else:
                    n = int(
                        math.log(
                            (epoch / self.T_0 * (self.T_mult - 1) + 1), self.T_mult
                        )
                    )
                    self.T_cur = epoch - self.T_0 * (self.T_mult**n - 1) / (
                        self.T_mult - 1
                    )
                    self.T_i = self.T_0 * self.T_mult ** (n)
            else:
                self.T_i = self.T_0
                self.T_cur = epoch
        self.last_epoch = math.floor(epoch)

        with _enable_get_lr_call(self):
            for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
                param_group["lr"] = lr

        self._last_lr = [group["lr"] for group in self.optimizer.param_groups]


class _SchedulePhase(TypedDict):
    end_step: float
    start_lr: str
    end_lr: str
    start_momentum: str
    end_momentum: str


class OneCycleLR(LRScheduler):
    r"""Sets the learning rate of each parameter group according to the 1cycle learning rate policy.

    The 1cycle policy anneals the learning rate from an initial learning rate to some maximum
    learning rate and then from that maximum learning rate to some minimum learning rate much
    lower than the initial learning rate.
    This policy was initially described in the paper `Super-Convergence:
    Very Fast Training of Neural Networks Using Large Learning Rates`_.

    The 1cycle learning rate policy changes the learning rate after every batch.
    `step` should be called after a batch has been used for training.

    This scheduler is not chainable.

    Note also that the total number of steps in the cycle can be determined in one
    of two ways (listed in order of precedence):

    #. A value for total_steps is explicitly provided.
    #. A number of epochs (epochs) and a number of steps per epoch
       (steps_per_epoch) are provided.
       In this case, the number of total steps is inferred by
       total_steps = epochs * steps_per_epoch

    You must either provide a value for total_steps or provide a value for both
    epochs and steps_per_epoch.

    The default behaviour of this scheduler follows the fastai implementation of 1cycle, which
    claims that "unpublished work has shown even better results by using only two phases". To
    mimic the behaviour of the original paper instead, set ``three_phase=True``.

    Args:
        optimizer (Optimizer): Wrapped optimizer.
        max_lr (float or list): Upper learning rate boundaries in the cycle
            for each parameter group.
        total_steps (int): The total number of steps in the cycle. Note that
            if a value is not provided here, then it must be inferred by providing
            a value for epochs and steps_per_epoch.
            Default: None
        epochs (int): The number of epochs to train for. This is used along
            with steps_per_epoch in order to infer the total number of steps in the cycle
            if a value for total_steps is not provided.
            Default: None
        steps_per_epoch (int): The number of steps per epoch to train for. This is
            used along with epochs in order to infer the total number of steps in the
            cycle if a value for total_steps is not provided.
            Default: None
        pct_start (float): The percentage of the cycle (in number of steps) spent
            increasing the learning rate.
            Default: 0.3
        anneal_strategy (str): {'cos', 'linear'}
            Specifies the annealing strategy: "cos" for cosine annealing, "linear" for
            linear annealing.
            Default: 'cos'
        cycle_momentum (bool): If ``True``, momentum is cycled inversely
            to learning rate between 'base_momentum' and 'max_momentum'.
            Default: True
        base_momentum (float or list): Lower momentum boundaries in the cycle
            for each parameter group. Note that momentum is cycled inversely
            to learning rate; at the peak of a cycle, momentum is
            'base_momentum' and learning rate is 'max_lr'.
            Default: 0.85
        max_momentum (float or list): Upper momentum boundaries in the cycle
            for each parameter group. Functionally,
            it defines the cycle amplitude (max_momentum - base_momentum).
            Note that momentum is cycled inversely
            to learning rate; at the start of a cycle, momentum is 'max_momentum'
            and learning rate is 'base_lr'
            Default: 0.95
        div_factor (float): Determines the initial learning rate via
            initial_lr = max_lr/div_factor
            Default: 25
        final_div_factor (float): Determines the minimum learning rate via
            min_lr = initial_lr/final_div_factor
            Default: 1e4
        three_phase (bool): If ``True``, use a third phase of the schedule to annihilate the
            learning rate according to 'final_div_factor' instead of modifying the second
            phase (the first two phases will be symmetrical about the step indicated by
            'pct_start').
        last_epoch (int): The index of the last batch. This parameter is used when
            resuming a training job. Since `step()` should be invoked after each
            batch instead of after each epoch, this number represents the total
            number of *batches* computed, not the total number of epochs computed.
            When last_epoch=-1, the schedule is started from the beginning.
            Default: -1

    Example:
        >>> # xdoctest: +SKIP
        >>> data_loader = torch.utils.data.DataLoader(...)
        >>> optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9)
        >>> scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=0.01, steps_per_epoch=len(data_loader), epochs=10)
        >>> for epoch in range(10):
        >>>     for batch in data_loader:
        >>>         train_batch(...)
        >>>         optimizer.step()
        >>>         scheduler.step()


    .. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates:
        https://arxiv.org/abs/1708.07120
    """

    def __init__(
        self,
        optimizer: Optimizer,
        max_lr: Union[float, list[float]],
        total_steps: Optional[int] = None,
        epochs: Optional[int] = None,
        steps_per_epoch: Optional[int] = None,
        pct_start: float = 0.3,
        anneal_strategy: Literal["cos", "linear"] = "cos",
        cycle_momentum: bool = True,
        base_momentum: Union[float, list[float]] = 0.85,
        max_momentum: Union[float, list[float]] = 0.95,
        div_factor: float = 25.0,
        final_div_factor: float = 1e4,
        three_phase: bool = False,
        last_epoch: int = -1,
    ):  # noqa: D107
        # Validate optimizer
        if not isinstance(optimizer, Optimizer):
            raise TypeError(f"{type(optimizer).__name__} is not an Optimizer")
        self.optimizer = optimizer

        # Validate total_steps
        if total_steps is not None:
            if total_steps <= 0 or not isinstance(total_steps, int):
                raise ValueError(
                    f"Expected positive integer total_steps, but got {total_steps}"
                )
            self.total_steps = total_steps
        elif epochs is not None and steps_per_epoch is not None:
            if not isinstance(epochs, int) or epochs <= 0:
                raise ValueError(f"Expected positive integer epochs, but got {epochs}")
            if not isinstance(steps_per_epoch, int) or steps_per_epoch <= 0:
                raise ValueError(
                    f"Expected positive integer steps_per_epoch, but got {steps_per_epoch}"
                )
            self.total_steps = epochs * steps_per_epoch
        else:
            raise ValueError(
                "You must define either total_steps OR (epochs AND steps_per_epoch)"
            )

        self._schedule_phases: list[_SchedulePhase]
        if three_phase:
            self._schedule_phases = [
                {
                    "end_step": float(pct_start * self.total_steps) - 1,
                    "start_lr": "initial_lr",
                    "end_lr": "max_lr",
                    "start_momentum": "max_momentum",
                    "end_momentum": "base_momentum",
                },
                {
                    "end_step": float(2 * pct_start * self.total_steps) - 2,
                    "start_lr": "max_lr",
                    "end_lr": "initial_lr",
                    "start_momentum": "base_momentum",
                    "end_momentum": "max_momentum",
                },
                {
                    "end_step": self.total_steps - 1,
                    "start_lr": "initial_lr",
                    "end_lr": "min_lr",
                    "start_momentum": "max_momentum",
                    "end_momentum": "max_momentum",
                },
            ]
        else:
            self._schedule_phases = [
                {
                    "end_step": float(pct_start * self.total_steps) - 1,
                    "start_lr": "initial_lr",
                    "end_lr": "max_lr",
                    "start_momentum": "max_momentum",
                    "end_momentum": "base_momentum",
                },
                {
                    "end_step": self.total_steps - 1,
                    "start_lr": "max_lr",
                    "end_lr": "min_lr",
                    "start_momentum": "base_momentum",
                    "end_momentum": "max_momentum",
                },
            ]

        # Validate pct_start
        if pct_start < 0 or pct_start > 1 or not isinstance(pct_start, float):
            raise ValueError(
                f"Expected float between 0 and 1 pct_start, but got {pct_start}"
            )

        # Validate anneal_strategy
        if anneal_strategy not in ["cos", "linear"]:
            raise ValueError(
                f"anneal_strategy must be one of 'cos' or 'linear', instead got {anneal_strategy}"
            )
        else:
            self._anneal_func_type = anneal_strategy

        # Initialize learning rate variables
        max_lrs = _format_param("max_lr", self.optimizer, max_lr)
        if last_epoch == -1:
            for idx, group in enumerate(self.optimizer.param_groups):
                group["initial_lr"] = max_lrs[idx] / div_factor
                group["max_lr"] = max_lrs[idx]
                group["min_lr"] = group["initial_lr"] / final_div_factor

        # Initialize momentum variables
        self.cycle_momentum = cycle_momentum
        if self.cycle_momentum:
            if (
                "momentum" not in self.optimizer.defaults
                and "betas" not in self.optimizer.defaults
            ):
                raise ValueError(
                    "optimizer must support momentum or beta1 with `cycle_momentum` option enabled"
                )
            self.use_beta1 = "betas" in self.optimizer.defaults
            max_momentums = _format_param("max_momentum", optimizer, max_momentum)
            base_momentums = _format_param("base_momentum", optimizer, base_momentum)
            if last_epoch == -1:
                for m_momentum, b_momentum, group in zip(
                    max_momentums, base_momentums, optimizer.param_groups
                ):
                    if self.use_beta1:
                        group["betas"] = (m_momentum, *group["betas"][1:])
                    else:
                        group["momentum"] = m_momentum
                    group["max_momentum"] = m_momentum
                    group["base_momentum"] = b_momentum

        super().__init__(optimizer, last_epoch)

    def _anneal_func(self, *args, **kwargs):
        if hasattr(self, "_anneal_func_type"):
            if self._anneal_func_type == "cos":
                return self._annealing_cos(*args, **kwargs)
            elif self._anneal_func_type == "linear":
                return self._annealing_linear(*args, **kwargs)
            else:
                raise ValueError(f"Unknown _anneal_func_type: {self._anneal_func_type}")
        else:
            # For BC
            return self.anneal_func(*args, **kwargs)  # type: ignore[attr-defined]

    @staticmethod
    def _annealing_cos(start, end, pct):
        """Cosine anneal from `start` to `end` as pct goes from 0.0 to 1.0."""
        cos_out = math.cos(math.pi * pct) + 1
        return end + (start - end) / 2.0 * cos_out

    @staticmethod
    def _annealing_linear(start, end, pct):
        """Linearly anneal from `start` to `end` as pct goes from 0.0 to 1.0."""
        return (end - start) * pct + start

    def get_lr(self):
        """Compute the learning rate of each parameter group."""
        _warn_get_lr_called_within_step(self)

        lrs = []
        step_num = self.last_epoch

        if step_num > self.total_steps:
            raise ValueError(
                f"Tried to step {step_num} times. The specified number of total steps is {self.total_steps}"  # noqa: UP032
            )

        for group in self.optimizer.param_groups:
            start_step = 0.0
            for i, phase in enumerate(self._schedule_phases):
                end_step = phase["end_step"]
                if step_num <= end_step or i == len(self._schedule_phases) - 1:
                    pct = (step_num - start_step) / (end_step - start_step)
                    computed_lr = self._anneal_func(
                        group[phase["start_lr"]], group[phase["end_lr"]], pct
                    )
                    if self.cycle_momentum:
                        computed_momentum = self._anneal_func(
                            group[phase["start_momentum"]],
                            group[phase["end_momentum"]],
                            pct,
                        )
                    break
                start_step = phase["end_step"]

            lrs.append(computed_lr)  # type: ignore[possibly-undefined]
            if self.cycle_momentum:
                if self.use_beta1:
                    group["betas"] = (computed_momentum, *group["betas"][1:])  # type: ignore[possibly-undefined]
                else:
                    group[
                        "momentum"
                    ] = computed_momentum  # type: ignore[possibly-undefined]

        return lrs
