"""
This module implements a neuro-symbolic architecture combining neural networks with symbolic reasoning.
"""

from typing import Dict, Any, List, Optional
try:
    import numpy as np
except Exception:
    np = None

class NeuroSymbolicEngine:
    def __init__(self):
        self.symbolic_rules = []
        self.neural_state = {}
        self.reasoning_cache = {}

    def add_symbolic_rule(self, rule: Dict[str, Any]):
        """
        Add a symbolic reasoning rule.
        
        Args:
            rule: A dictionary defining the rule structure
        """
        self.symbolic_rules.append(rule)

    def process_neural_input(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Process input through the neural component.
        
        Args:
            input_data: Data to be processed by neural networks
            
        Returns:
            Processed neural outputs
        """
        # Simulate neural processing
        if np is not None:
            processed_data = {
                "features": np.random.random(10).tolist(),  # Simulated feature extraction
                "confidence": float(np.random.random()),
                "embedding": np.random.random(128).tolist()  # Simulated embedding
            }
        else:
            import random
            processed_data = {
                "features": [random.random() for _ in range(10)],
                "confidence": float(random.random()),
                "embedding": [random.random() for _ in range(128)]
            }
        
        self.neural_state.update(processed_data)
        return processed_data

    def apply_symbolic_reasoning(self, context: Dict[str, Any]) -> Dict[str, Any]:
        """
        Apply symbolic reasoning rules to the context.
        
        Args:
            context: The context to apply reasoning to
            
        Returns:
            Results of symbolic reasoning
        """
        results = {
            "applied_rules": [],
            "inferences": [],
            "confidence": 0.0
        }
        
        for rule in self.symbolic_rules:
            if self._check_rule_applicability(rule, context):
                inference = self._apply_rule(rule, context)
                results["applied_rules"].append(rule)
                results["inferences"].append(inference)
                
        if np is not None:
            results["confidence"] = float(np.mean([inf.get("confidence", 0) for inf in results["inferences"]])) if results["inferences"] else 0.0
        else:
            vals = [inf.get("confidence", 0) for inf in results["inferences"]]
            results["confidence"] = float(sum(vals)/len(vals)) if vals else 0.0
        
        return results

    def combine_neural_symbolic(self, neural_output: Dict[str, Any], symbolic_output: Dict[str, Any]) -> Dict[str, Any]:
        """
        Combine neural and symbolic processing results.
        
        Args:
            neural_output: Results from neural processing
            symbolic_output: Results from symbolic reasoning
            
        Returns:
            Combined neuro-symbolic output
        """
        if np is not None:
            confidence = float(np.mean([
                neural_output.get("confidence", 0),
                symbolic_output.get("confidence", 0)
            ]))
        else:
            confidence = float((neural_output.get("confidence", 0) + symbolic_output.get("confidence", 0))/2)

        combined = {
            "neural_features": neural_output.get("features", []),
            "symbolic_inferences": symbolic_output.get("inferences", []),
            "confidence": confidence,
            "combined_embedding": self._combine_embeddings(
                neural_output.get("embedding", []),
                symbolic_output.get("embedding", [])
            )
        }
        
        return combined

    def reason(self, input_data: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]:
        """
        Perform complete neuro-symbolic reasoning.
        
        Args:
            input_data: Input data to process
            context: Context for reasoning
            
        Returns:
            Complete reasoning results
        """
        # Process through neural component
        neural_output = self.process_neural_input(input_data)
        
        # Apply symbolic reasoning
        symbolic_output = self.apply_symbolic_reasoning(context)
        
        # Combine results
        result = self.combine_neural_symbolic(neural_output, symbolic_output)
        
        # Cache results
        cache_key = str(hash(str(input_data) + str(context)))
        self.reasoning_cache[cache_key] = result
        
        return result

    def _check_rule_applicability(self, rule: Dict[str, Any], context: Dict[str, Any]) -> bool:
        """
        Check if a symbolic rule is applicable in the given context.
        """
        # Simplified rule checking
        conditions = rule.get("conditions", [])
        return all(
            context.get(cond["key"]) == cond["value"]
            for cond in conditions
        )

    def _apply_rule(self, rule: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]:
        """
        Apply a symbolic rule to derive new knowledge.
        """
        return {
            "rule_id": rule.get("id"),
            "inference": rule.get("conclusion", ""),
            "confidence": np.random.random()  # Simplified confidence
        }

    def _combine_embeddings(self, neural_embedding: List[float], symbolic_embedding: List[float]) -> List[float]:
        """
        Combine neural and symbolic embeddings.
        """
        # If either embedding is empty, return the other
        if not neural_embedding:
            return symbolic_embedding
        if not symbolic_embedding:
            return neural_embedding
            
        # If lengths differ, pad shorter one with zeros
        max_len = max(len(neural_embedding), len(symbolic_embedding))
        neural_embedding = neural_embedding + [0] * (max_len - len(neural_embedding))
        symbolic_embedding = symbolic_embedding + [0] * (max_len - len(symbolic_embedding))
        
        # Combine with equal weights
        return [(n + s) / 2 for n, s in zip(neural_embedding, symbolic_embedding)]

    def get_cached_reasoning(self, input_data: Dict[str, Any], context: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """
        Retrieve cached reasoning results if available.
        """
        cache_key = str(hash(str(input_data) + str(context)))
        return self.reasoning_cache.get(cache_key)

    def clear_cache(self):
        """
        Clear the reasoning cache.
        """
        self.reasoning_cache = {}