import wandb
from torch.utils.tensorboard import SummaryWriter
class AverageMeter:
	'Computes and stores the average and current value'
	def __init__(self,name,fmt=':f'):self.name=name;self.fmt=fmt;self.reset()
	def reset(self):self.val=0;self.avg=0;self.sum=0;self.count=0
	def update(self,val,n=1):
		if self.count+n!=0:self.val=val;self.sum+=val*n;self.count+=n;self.avg=self.sum/self.count
	def __str__(self):fmtstr='{name} {val'+self.fmt+'} ({avg'+self.fmt+'})';return fmtstr.format(**self.__dict__)
class ProgressMeter:
	def __init__(self,num_batches,meters,prefix=''):self.batch_fmtstr=self._get_batch_fmtstr(num_batches);self.meters=meters;self.prefix=prefix
	def display(self,batch):entries=[self.prefix+self.batch_fmtstr.format(batch)];entries+=[str(meter)for meter in self.meters];print('\t'.join(entries),flush=True)
	def _get_batch_fmtstr(self,num_batches):num_digits=len(str(num_batches//1));fmt='{:'+str(num_digits)+'d}';return f"[{fmt}/{fmt.format(num_batches)}]"
class BaseLogger:
	def __init__(self)->None:0
	def write(self,data)->None:0
	def close(self)->None:0
	def write_images(self,imgs,captions)->None:0
class WaBLogger(BaseLogger):
	def __init__(self,id:str,project:str,group:str,dir:str,args_dict=None,model=None,sweep=False,mode='online'):
		super().__init__();self.model=model
		if sweep:self.writer=wandb.init()
		else:self.writer=wandb.init(project=project,id=id,group=group,dir=dir,config=args_dict,resume='allow',mode=mode)
		if model:wandb.watch(model,log='none',log_freq=1)
	def write(self,data:dict):'Log attributes from the data dictionary.\n\n        Args:\n            data (dict): dictionary containing pairs of (k,v), where\n                key is the name of the attribute and v its value (to\n                be logged).\n        ';self.writer.log(data)
	def write_images(self,imgs,captions):images={cap:wandb.Image(img,caption=cap)for(img,cap)in zip(imgs,captions)};wandb.log({'artifacts':images})
	def get_run_id(self):return self.writer._name
	def close(self):self.writer.finish()
class TBLogger:
	def __init__(self,path:str):self.writer=SummaryWriter(path)
	def write(self,data:dict):
		'Log attributes from the data dictionary.\n\n        Args:\n            data (dict): dictionary containing pairs of (k,v), where\n                key is the name of the attribute and v its value (to\n                be logged).\n        ';assert hasattr(data,'epoch'),"'epoch' must be passed at each logging step";epoch=data.pop('epoch')
		for(k,v)in enumerate(data):self.writer.add_scalar(k,v,epoch)
	def close(self):self.writer.close()