Metrics not logged properly in Pytorch Lightening

Question

The feature of logging is not working fine. It is giving following logs on console -->

v_num:z3_3  val_loss:3.105  val_kappa:0.34   val_accuracy:0.295 
            train_loss:2.436    train_kappa: nan  train_accuracy:0.0

train_loss is working fine !!

Here I have following doubts --> a) Why training_accuracy is staying at 0 irrespective of epochs when it is not the case with validation accuracy. b) why training kappa is nan, when it is not the case with that of validation kappa c) Why is validation metrics displayed first, I guess that train metrics should be above it, as model first gets trained before put on evaluation mode.

I'm pasting entire code so that there is no scope of ambiguity.

class Classifier(pl.LightningModule):
  def __init__(self, model_obj):
    super().__init__()
    self.model = model_obj.model
    self.config = model_obj.config
    self.layer_lr = model_obj.layer_lr

    self.kappa = torchmetrics.CohenKappa(task = 'multiclass' , num_classes = self.config['num_classes'], weights = 'quadratic')
    self.accuracy = torchmetrics.Accuracy(task = 'multiclass' , num_classes = self.config['num_classes'])
    self.criterion = torch.nn.CrossEntropyLoss()

  def training_step(self, batch, batch_idx):
    x, y = batch
    y_hat = self.model(x)
    loss = self.criterion(y_hat, y.long())
    self.log("train_loss", loss,on_step = False ,on_epoch=True, prog_bar=True, logger=True)
    self.accuracy(y_hat, y)
    self.kappa(y_hat, y)
    return loss
  
  def validation_step(self, batch, batch_idx):
    x, y = batch
    y_hat = self.model(x)
    loss = self.criterion(y_hat, y.long())
    self.log("val_loss", loss, on_epoch=True, prog_bar=True, logger=True)
    self.accuracy(y_hat, y)
    self.kappa(y_hat, y)
    return loss
  
  def test_step(self, batch, batch_idx):
    x, y = batch
    y_hat = self.model(x)
    loss = self.criterion(y_hat, y.long())
    self.log("test_loss", loss, on_epoch=True, prog_bar=True, logger=True)
    self.accuracy(y_hat, y)
    self.kappa(y_hat, y)
    return loss
  
  def on_train_epoch_end(self):
    
    self.log("train_kappa", self.kappa,on_step=False,  on_epoch=True, prog_bar=True, logger=True)
    self.log("train_accuracy", self.accuracy, on_epoch=True,prog_bar=True, logger=True)

  def on_validation_epoch_end(self):
    
    self.log("val_kappa", self.kappa,on_step = False, on_epoch=True, prog_bar=True, logger=True)
    self.log("val_accuracy", self.accuracy, on_epoch=True,prog_bar=True, logger=True)

  def on_test_epoch_end(self):
    
    self.log("test_kappa", self.kappa,on_step = False,  on_epoch=True, prog_bar=True, logger=True)
    self.log("test_accuracy", self.accuracy, on_epoch=True,prog_bar=True, logger=True)

  
  def configure_optimizers(self):
    optim =  torch.optim.Adam(self.layer_lr, lr = self.config['lr'])   # https://pytorch.org/docs/stable/optim.html
    lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optim, patience=3, factor=0.5, threshold=0.001, cooldown =2,verbose=True)
    return [optim], [{'scheduler': lr_scheduler, 'interval': 'epoch', 'monitor': 'train_loss', 'name': 'lr_scheduler'}]

Thanks in Advance!!

I tried searching for this issue, but I failed to find any related issue as well. That made me believe that this is some new issue haven't been faced by anybody till now.