import torch
import torch.nn as nn

class PhysicsTransformer(nn.Module):
    """PIBERT core architecture for physics-informed transformer modeling."""
    
    def __init__(self, 
                 input_dim,
                 hidden_dim,
                 num_layers,
                 num_heads,
                 fourier_features=True,
                 wavelet_features=True,
                 physics_attention=True):
        super().__init__()
        # Core implementation would go here
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.num_layers = num_layers
        self.num_heads = num_heads
        self.fourier_features = fourier_features
        self.wavelet_features = wavelet_features
        self.physics_attention = physics_attention
        
        # Placeholder for actual implementation
        self.placeholder = nn.Linear(input_dim, hidden_dim)
        
    def forward(self, x, coords=None, pde_residuals=None):
        """Forward pass through the physics-informed transformer.
        
        Args:
            x: Input tensor of shape [batch_size, seq_length, input_dim]
            coords: Spatial coordinates for physics-informed attention
            pde_residuals: Precomputed PDE residuals for attention biasing
            
        Returns:
            Output tensor of shape [batch_size, seq_length, hidden_dim]
        """
        # In a real implementation, this would include:
        # 1. Hybrid spectral embeddings
        # 2. Physics-biased attention
        # 3. Transformer layers
        return self.placeholder(x)
        
    def predict(self, x, coords=None):
        """Prediction method for PDE solutions."""
        return self(x, coords)
        
    def compute_pde_residuals(self, x, coords, pde_equation):
        """Compute residuals of the PDE at given points."""
        # Placeholder for actual residual computation
        return torch.zeros_like(x)
