"""
Hugging Face compatible configuration for existing Space
This extends your existing config without breaking it
"""

try:
    from transformers import PretrainedConfig
    TRANSFORMERS_AVAILABLE = True
except ImportError:
    TRANSFORMERS_AVAILABLE = False
    # Fallback configuration
    class PretrainedConfig:
        def __init__(self, **kwargs):
            for key, value in kwargs.items():
                setattr(self, key, value)


class HFResNet18DetectorConfig(PretrainedConfig):
    """
    Hugging Face compatible configuration for your existing model
    Works alongside your existing training config
    """
    
    model_type = "resnet18-detector"
    
    def __init__(
        self,
        num_classes: int = 2,
        image_size: int = 224,
        architecture: str = "resnet18",
        dropout_rate: float = 0.5,
        freeze_backbone: bool = False,
        pretrained_weights: str = "IMAGENET1K_V1",
        label_smoothing: float = 0.1,  # Anti-overfitting: Label smoothing
        weight_decay: float = 0.1,      # Anti-overfitting: L2 regularization
        max_grad_norm: float = 1.0,     # Anti-overfitting: Gradient clipping
        **kwargs
    ):
        """
        Initialize HF compatible config with anti-overfitting parameters
        """
        self.num_classes = num_classes
        self.image_size = image_size
        self.architecture = architecture
        self.dropout_rate = dropout_rate
        self.freeze_backbone = freeze_backbone
        self.pretrained_weights = pretrained_weights
        self.label_smoothing = label_smoothing
        self.weight_decay = weight_decay
        self.max_grad_norm = max_grad_norm
        
        if TRANSFORMERS_AVAILABLE:
            super().__init__(**kwargs)
        else:
            for key, value in kwargs.items():
                setattr(self, key, value)


# Register for auto-loading if transformers is available
if TRANSFORMERS_AVAILABLE:
    try:
        HFResNet18DetectorConfig.register_for_auto_class()
    except:
        pass