# trainer.py
from transformers import Trainer, TrainingArguments
from torch.utils.data import DataLoader
import torch
import torch.nn.functional as F

class MTPTrainer(Trainer):
    """
    Custom trainer for Multi-Token Prediction training.
    """
    def __init__(self, model, args=None, train_dataset=None, eval_dataset=None, **kwargs):
        super().__init__(
            model=model,
            args=args,
            train_dataset=train_dataset,
            eval_dataset=eval_dataset,
            **kwargs
        )
        self.use_mtp_training = True

    def compute_loss(self, model, inputs, return_outputs=False):
        """
        Compute loss during training - handles both MTP and standard LM training.
        """
        labels = inputs.get("labels")
        outputs = model(**inputs, labels=labels, use_mtp_training=self.use_mtp_training)
        loss = outputs.loss
        
        return (loss, outputs) if return_outputs else loss

    def train_step_with_mtp(self, model, inputs):
        """
        Specialized training step for MTP training.
        """
        model.set_mtp_training(True)
        return self.training_step(model, inputs)

    def train_step_with_lm(self, model, inputs):
        """
        Standard language modeling training step.
        """
        model.set_mtp_training(False)
        return self.training_step(model, inputs)


class RLTrainer:
    """
    Separate trainer class for Reinforcement Learning training.
    This can use the generate_with_logprobs method from your model.
    """
    def __init__(self, model, tokenizer, rl_config=None):
        self.model = model
        self.tokenizer = tokenizer
        self.rl_config = rl_config or {}
        
    def rl_training_step(self, input_ids, old_log_probs, old_action_probs, **kwargs):
        """
        Perform an RL training step using the model's generate_with_logprobs method
        and the reward functions from rl_utils.
        """
        # Import RL utilities
        from .rl_utils import (
            batch_compute_rewards, 
            compute_ppo_loss, 
            compute_kl_divergence,
            compute_entropy_bonus,
            AdaptiveKLController
        )
        
        # This would call the generate_with_logprobs method from your model
        # and then compute RL-specific losses
        pass