import torch 
from torch .optim .lr_scheduler import _LRScheduler 


class WarmupCosineAnnealingLR (_LRScheduler ):
    def __init__ (
    self ,
    optimizer ,
    warmup_iterations :int ,
    decay_iterations :int ,
    max_iterations :int ,
    eta_min :float =0.0 ,
    last_iteration :int =-1 ,
    )->None :
        self .warmup_iterations :int =warmup_iterations 
        self .decay_iterations :int =decay_iterations 
        self .max_iterations :int =max_iterations 
        self .eta_min :float =eta_min 
        super (WarmupCosineAnnealingLR ,self ).__init__ (optimizer ,last_epoch =last_iteration )

    def get_lr (self ):
        if self .last_epoch <self .warmup_iterations :

            warmup_ratio :float =self .last_epoch /self .warmup_iterations 
            return [
            max (self .eta_min +(base_lr -self .eta_min )*warmup_ratio ,self .eta_min )
            for base_lr in self .base_lrs 
            ]
        elif self .last_epoch <self .decay_iterations :

            progress :float =(self .last_epoch -self .warmup_iterations )/(
            self .decay_iterations -self .warmup_iterations 
            )
            cosine_decay =0.5 *(1 +torch .cos (torch .tensor (progress )*3.14159 ))
            return [
            max (self .eta_min +(base_lr -self .eta_min )*cosine_decay .item (),self .eta_min )
            for base_lr in self .base_lrs 
            ]
        else :

            return [self .eta_min for _ in self .base_lrs ]
