import math
import torch
from torch.optim.lr_scheduler import _LRScheduler

from diffusers.optimization import (
    Union, SchedulerType, Optional,
    Optimizer, TYPE_TO_SCHEDULER_FUNCTION
)

def get_scheduler(
    name: Union[str, SchedulerType],
    optimizer: Optimizer,
    num_warmup_steps: Optional[int] = None,
    num_training_steps: Optional[int] = None,
    **kwargs
):
    """
    Added kwargs vs diffuser's original implementation

    Unified API to get any scheduler from its name.

    Args:
        name (`str` or `SchedulerType`):
            The name of the scheduler to use.
        optimizer (`torch.optim.Optimizer`):
            The optimizer that will be used during training.
        num_warmup_steps (`int`, *optional*):
            The number of warmup steps to do. This is not required by all schedulers (hence the argument being
            optional), the function will raise an error if it's unset and the scheduler type requires it.
        num_training_steps (`int``, *optional*):
            The number of training steps to do. This is not required by all schedulers (hence the argument being
            optional), the function will raise an error if it's unset and the scheduler type requires it.
    """
    name = SchedulerType(name)
    schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name]
    if name == SchedulerType.CONSTANT:
        return schedule_func(optimizer, **kwargs)

    # All other schedulers require `num_warmup_steps`
    if num_warmup_steps is None:
        raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument.")

    if name == SchedulerType.CONSTANT_WITH_WARMUP:
        return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, **kwargs)

    # All other schedulers require `num_training_steps`
    if num_training_steps is None:
        raise ValueError(f"{name} requires `num_training_steps`, please provide that argument.")

    return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, **kwargs)


# From https://github.com/katsura-jp/pytorch-cosine-annealing-with-warmup
class CosineAnnealingWarmupRestarts(_LRScheduler):
    """
    optimizer (Optimizer): Wrapped optimizer.
    first_cycle_steps (int): First cycle step size.
    cycle_mult(float): Cycle steps magnification. Default: -1.
    max_lr(float): First cycle's max learning rate. Default: 0.1.
    min_lr(float): Min learning rate. Default: 0.001.
    warmup_steps(int): Linear warmup step size. Default: 0.
    gamma(float): Decrease rate of max learning rate by cycle. Default: 1.
    last_epoch (int): The index of last epoch. Default: -1.
    """

    def __init__(
        self,
        optimizer: torch.optim.Optimizer,
        first_cycle_steps: int,
        cycle_mult: float = 1.0,
        max_lr: float = 0.1,
        min_lr: float = 0.001,
        warmup_steps: int = 0,
        gamma: float = 1.0,
        last_epoch: int = -1,
    ):
        assert warmup_steps < first_cycle_steps

        self.first_cycle_steps = first_cycle_steps  # first cycle step size
        self.cycle_mult = cycle_mult  # cycle steps magnification
        self.base_max_lr = max_lr  # first max learning rate
        self.max_lr = max_lr  # max learning rate in the current cycle
        self.min_lr = min_lr  # min learning rate
        self.warmup_steps = warmup_steps  # warmup step size
        self.gamma = gamma  # decrease rate of max learning rate by cycle

        self.cur_cycle_steps = first_cycle_steps  # first cycle step size
        self.cycle = 0  # cycle count
        self.step_in_cycle = last_epoch  # step size of the current cycle

        super(CosineAnnealingWarmupRestarts, self).__init__(optimizer, last_epoch)

        # set learning rate min_lr
        self.init_lr()

    def init_lr(self):
        self.base_lrs = []
        for param_group in self.optimizer.param_groups:
            param_group["lr"] = self.min_lr
            self.base_lrs.append(self.min_lr)
    
    def get_lr(self):
        if self.step_in_cycle == -1:
            return self.base_lrs
        elif self.step_in_cycle < self.warmup_steps:
            return [
                (self.max_lr - base_lr) * self.step_in_cycle / self.warmup_steps
                + base_lr
                for base_lr in self.base_lrs
            ]
        else:
            return [
                base_lr
                + (self.max_lr - base_lr)
                * (
                    1
                    + math.cos(
                        math.pi
                        * (self.step_in_cycle - self.warmup_steps)
                        / (self.cur_cycle_steps - self.warmup_steps)
                    )
                )
                / 2
                for base_lr in self.base_lrs
            ]

    def step(self, epoch=None):
        if epoch is None:
            epoch = self.last_epoch + 1
            self.step_in_cycle = self.step_in_cycle + 1
            if self.step_in_cycle >= self.cur_cycle_steps:
                self.cycle += 1
                self.step_in_cycle = self.step_in_cycle - self.cur_cycle_steps
                self.cur_cycle_steps = (
                    int((self.cur_cycle_steps - self.warmup_steps) * self.cycle_mult)
                    + self.warmup_steps
                )
        else:
            if epoch >= self.first_cycle_steps:
                if self.cycle_mult == 1.0:
                    self.step_in_cycle = epoch % self.first_cycle_steps
                    self.cycle = epoch // self.first_cycle_steps
                else:
                    n = int(
                        math.log(
                            (
                                epoch / self.first_cycle_steps * (self.cycle_mult - 1)
                                + 1
                            ),
                            self.cycle_mult,
                        )
                    )
                    self.cycle = n
                    self.step_in_cycle = epoch - int(
                        self.first_cycle_steps
                        * (self.cycle_mult**n - 1)
                        / (self.cycle_mult - 1)
                    )
                    self.cur_cycle_steps = self.first_cycle_steps * self.cycle_mult ** (
                        n
                    )
            else:
                self.cur_cycle_steps = self.first_cycle_steps
                self.step_in_cycle = epoch

        self.max_lr = self.base_max_lr * (self.gamma**self.cycle)
        self.last_epoch = math.floor(epoch)
        for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
            param_group["lr"] = lr