import torch
import torch.nn as nn
import pywt  # Will need to add to requirements

class WaveletEmbedding(nn.Module):
    """Wavelet embedding layer for capturing localized features in PDE solutions."""
    
    def __init__(self, input_dim, hidden_dim, wavelet='db4', level=3):
        super().__init__()
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.wavelet = wavelet
        self.level = level
        
        # Learnable parameters for wavelet coefficients
        self.coeff_weights = nn.Parameter(torch.randn(level + 1, input_dim))
        
        # Projection to hidden dimension
        self.projection = nn.Linear(input_dim * (level + 1), hidden_dim)
        
    def forward(self, x):
        """
        Args:
            x: Input tensor of shape [batch_size, seq_length, input_dim]
            
        Returns:
            Embedded tensor of shape [batch_size, seq_length, hidden_dim]
        """
        # In practice, this would perform actual wavelet transforms
        # This is a simplified placeholder
        batch_size, seq_length, input_dim = x.shape
        
        # Placeholder for wavelet coefficients
        # In real implementation, would use pywt or custom wavelet transform
        coeffs = x  # This would be replaced with actual wavelet coefficients
        
        # Weight and combine coefficients
        weighted_coeffs = coeffs * self.coeff_weights
        
        # Project to hidden dimension
        return self.projection(weighted_coeffs)
