from typing import Optional, Dict, Any
import torch
from pathlib import Path
import json
import logging

class QuantumTrainer:
    """Quantum-enhanced training module for language models."""
    
    def __init__(self, 
                 model_name: str = "phi-4",
                 device: Optional[str] = None,
                 verbose: bool = False):
        """Initialize the quantum trainer.
        
        Args:
            model_name: Name of the base model to use
            device: Device to use for training (cuda/cpu)
            verbose: Whether to print detailed logs
        """
        self.model_name = model_name
        self.device = device or ('cuda' if torch.cuda.is_available() else 'cpu')
        self.verbose = verbose
        
        # Set up logging
        logging.basicConfig(
            level=logging.INFO if verbose else logging.WARNING,
            format='%(asctime)s - %(levelname)s - %(message)s'
        )
        self.logger = logging.getLogger(__name__)
        
        # Will be initialized during setup
        self.model = None
        self.tokenizer = None
        self.optimizer = None
        self.quantum_optimizer = None
    
    def setup(self, config: Dict[str, Any]):
        """Set up the training environment.
        
        Args:
            config: Training configuration dictionary
        """
        self.logger.info("Setting up training environment...")
        self.logger.info(f"Using device: {self.device}")
        
        # To be implemented:
        # - Load model and tokenizer
        # - Initialize optimizers
        # - Set up quantum optimization
        self.logger.info("Training setup will be implemented in next phase")
    
    def load_data(self, data_dir: str):
        """Load training data from directory.
        
        Args:
            data_dir: Directory containing the training data
            
        Raises:
            FileNotFoundError: If data directory or required files don't exist
        """
        data_path = Path(data_dir)
        if not data_path.exists():
            raise FileNotFoundError(f"Data directory not found: {data_dir}")
            
        self.logger.info(f"Loading data from {data_dir}")
        
        required_files = ["train.json", "eval.json", "quantum_problems.json"]
        for file in required_files:
            if not (data_path / file).exists():
                raise FileNotFoundError(f"Required file not found: {file}")
        
        # Load training data
        with open(data_path / "train.json", "r") as f:
            self.train_data = json.load(f)
        
        # Load evaluation data
        with open(data_path / "eval.json", "r") as f:
            self.eval_data = json.load(f)
            
        # Load quantum optimization problems
        with open(data_path / "quantum_problems.json", "r") as f:
            self.quantum_problems = json.load(f)
            
        self.logger.info(f"Loaded {len(self.train_data)} training samples")
        self.logger.info(f"Loaded {len(self.eval_data)} evaluation samples")
        self.logger.info(f"Loaded {len(self.quantum_problems)} quantum problems")
    
    def train(self, 
              num_epochs: int,
              batch_size: int,
              learning_rate: float,
              save_dir: Optional[str] = None,
              **kwargs):
        """Train the model with quantum optimization.
        
        Args:
            num_epochs: Number of training epochs
            batch_size: Training batch size
            learning_rate: Learning rate
            save_dir: Directory to save checkpoints
            **kwargs: Additional training arguments
        """
        self.logger.info("Starting training...")
        self.logger.info(f"Training parameters: epochs={num_epochs}, "
                        f"batch_size={batch_size}, lr={learning_rate}")
        
        # To be implemented:
        # - Training loop with quantum optimization
        # - Model checkpointing
        # - Progress tracking
        self.logger.info("Training loop will be implemented in next phase")
    
    def evaluate(self, test_data: Optional[str] = None):
        """Evaluate the model on test data.
        
        Args:
            test_data: Optional path to test data file
        """
        self.logger.info("Starting evaluation...")
        
        # To be implemented:
        # - Model evaluation
        # - Metrics computation
        # - Results logging
        self.logger.info("Evaluation will be implemented in next phase")
    
    def save_model(self, save_dir: str):
        """Save the trained model and configuration.
        
        Args:
            save_dir: Directory to save the model
        """
        save_path = Path(save_dir)
        save_path.mkdir(parents=True, exist_ok=True)
        
        self.logger.info(f"Saving model to {save_dir}")
        
        # To be implemented:
        # - Save model weights
        # - Save configuration
        # - Save tokenizer
        self.logger.info("Model saving will be implemented in next phase")
    
    def load_model(self, model_path: str):
        """Load a trained model.
        
        Args:
            model_path: Path to the saved model
            
        Raises:
            Exception: If model path doesn't exist or model loading fails
        """
        model_path = Path(model_path)
        if not model_path.exists():
            raise Exception(f"Model path does not exist: {model_path}")
            
        self.logger.info(f"Loading model from {model_path}")
        
        # To be implemented:
        # - Load model weights
        # - Load configuration
        # - Load tokenizer
        self.logger.info("Model loading will be implemented in next phase")