from dataclasses import dataclass
from pathlib import Path
import torch

@dataclass
class TrainingConfig:
    """Configuration for model training"""
    
    # Model parameters
    model_name: str = "microsoft/deberta-v3-large"
    dropout: float = 0.1
    
    # Training parameters
    num_epochs: int = 5
    batch_size: int = 8
    learning_rate: float = 2e-5
    warmup_ratio: float = 0.1
    weight_decay: float = 0.01
    max_grad_norm: float = 1.0
    
    # Data parameters
    max_length: int = 512
    train_ratio: float = 0.8
    
    # Output parameters
    output_dir: Path = Path("outputs")
    save_steps: int = 100
    eval_steps: int = 50
    
    # Device
    device: str = "cuda" if torch.cuda.is_available() else "cpu"
    
    def __post_init__(self):
        """Create output directory if it doesn't exist"""
        self.output_dir.mkdir(parents=True, exist_ok=True)

# Test code
if __name__ == "__main__":
    # Create default config
    default_config = TrainingConfig()
    
    print("\n=== Default Configuration ===")
    print(f"Model name: {default_config.model_name}")
    print(f"Batch size: {default_config.batch_size}")
    print(f"Learning rate: {default_config.learning_rate}")
    print(f"Device: {default_config.device}")
    
    # Create custom config
    custom_config = TrainingConfig(
        batch_size=16,
        num_epochs=10,
        learning_rate=1e-5
    )
    
    print("\n=== Custom Configuration ===")
    print(f"Model name: {custom_config.model_name}")  # Uses default
    print(f"Batch size: {custom_config.batch_size}")  # Customized
    print(f"Learning rate: {custom_config.learning_rate}")  # Customized
    print(f"Number of epochs: {custom_config.num_epochs}")  # Customized