"""Symbolic layer with causal constraints for CausalTorch."""

import torch
import torch.nn as nn


class CausalSymbolicLayer(nn.Module):
    """Layer that enforces symbolic causal rules in the latent space.
    
    This layer directly modifies latent variables according to causal rules,
    ensuring that dependent variables change when their causes are modified.
    
    Args:
        causal_rules (dict, optional): Dictionary of causal rules
    """
    def __init__(self, causal_rules=None):
        super().__init__()
        self.causal_rules = causal_rules or {}
    
    def forward(self, z):
        """Apply causal constraints to latent variables.
        
        Args:
            z (torch.Tensor): Latent tensor where dimensions represent causal variables
            
        Returns:
            torch.Tensor: Modified latent tensor with causal constraints applied
        """
        # Example: enforce "rain → wet ground" rule
        if "rain" in self.causal_rules:
            rain_idx = 0  # Assume rain is the first dimension
            wet_ground_idx = 1  # Assume ground wetness is the second dimension
            
            # Get rule parameters
            rule = self.causal_rules["rain"]
            strength = rule.get("strength", 0.9)
            threshold = rule.get("threshold", 0.5)
            
            # Apply rule: if rain > threshold, ground should be wet
            rain_intensity = z[:, rain_idx]
            ground_wetness = torch.sigmoid((rain_intensity - threshold) * 10) * strength
            z[:, wet_ground_idx] = ground_wetness
        
        return z