"""
Reasoning components for hybrid neuro-symbolic models.

This module provides implementations of reasoning systems that combine neural
networks with symbolic reasoning for improved interpretability and reasoning
capabilities.
"""

import numpy as np
from typing import Dict, List, Tuple, Set, Optional, Union, Any, Callable
from collections import defaultdict

from neurenix.tensor import Tensor
from neurenix.nn.module import Module
from neurenix.nn.functional import sigmoid, tanh, relu, softmax

from .symbolic import SymbolicReasoner, LogicProgram, RuleSet, SymbolicKnowledgeBase


class ConstraintSatisfaction(Module):
    """Constraint satisfaction for hybrid neuro-symbolic models."""
    
    def __init__(self, neural_model: Module, constraints: List[Callable[[Tensor], Tensor]],
                constraint_weight: float = 1.0):
        """Initialize a constraint satisfaction model.
        
        Args:
            neural_model: The neural network model
            constraints: List of constraint functions
            constraint_weight: Weight for the constraint loss
        """
        super().__init__()
        self.neural_model = neural_model
        self.constraints = constraints
        self.constraint_weight = constraint_weight
        
    def forward(self, x: Tensor) -> Tensor:
        """Forward pass through the constraint satisfaction model.
        
        Args:
            x: Input tensor
            
        Returns:
            Output tensor from the neural model
        """
        return self.neural_model(x)
    
    def constraint_loss(self, y_pred: Tensor) -> Tensor:
        """Compute the constraint loss.
        
        Args:
            y_pred: Predicted output tensor
            
        Returns:
            Constraint loss tensor
        """
        constraint_losses = []
        
        for constraint in self.constraints:
            constraint_value = constraint(y_pred)
            constraint_losses.append(constraint_value)
            
        if constraint_losses:
            return sum(constraint_losses) / len(constraint_losses)
        else:
            return Tensor([0.0])
    
    def combined_loss(self, y_pred: Tensor, y_true: Tensor,
                    loss_fn: Callable[[Tensor, Tensor], Tensor]) -> Tensor:
        """Compute combined loss with constraint satisfaction.
        
        Args:
            y_pred: Predicted output tensor
            y_true: Ground truth output tensor
            loss_fn: Loss function for the neural model's predictions
            
        Returns:
            Combined loss tensor
        """
        neural_loss = loss_fn(y_pred, y_true)
        
        constraint_loss = self.constraint_loss(y_pred)
        
        total_loss = neural_loss + self.constraint_weight * constraint_loss
        return total_loss


class LogicalInference(Module):
    """Logical inference for hybrid neuro-symbolic models."""
    
    def __init__(self, neural_model: Module, symbolic_reasoner: SymbolicReasoner,
                integration_mode: str = 'sequential'):
        """Initialize a logical inference model.
        
        Args:
            neural_model: The neural network model
            symbolic_reasoner: The symbolic reasoner
            integration_mode: How to integrate neural and symbolic components
                ('sequential', 'parallel', or 'interactive')
        """
        super().__init__()
        self.neural_model = neural_model
        self.symbolic_reasoner = symbolic_reasoner
        self.integration_mode = integration_mode
        
        if integration_mode not in ['sequential', 'parallel', 'interactive']:
            raise ValueError(f"Invalid integration mode: {integration_mode}")
        
    def forward(self, x: Tensor, symbolic_queries: Optional[List[str]] = None) -> Dict[str, Any]:
        """Forward pass through the logical inference model.
        
        Args:
            x: Input tensor
            symbolic_queries: Optional list of symbolic queries to evaluate
            
        Returns:
            Dictionary of inference results
        """
        if self.integration_mode == 'sequential':
            neural_output = self.neural_model(x)
            
            if symbolic_queries is not None:
                symbolic_output = self.symbolic_reasoner.to_tensor(symbolic_queries)
                
                return {
                    'neural_output': neural_output,
                    'symbolic_output': symbolic_output,
                    'combined_output': neural_output
                }
            else:
                return {
                    'neural_output': neural_output,
                    'combined_output': neural_output
                }
                
        elif self.integration_mode == 'parallel':
            neural_output = self.neural_model(x)
            
            if symbolic_queries is not None:
                symbolic_output = self.symbolic_reasoner.to_tensor(symbolic_queries)
                
                combined_output = neural_output * (1.0 + symbolic_output.unsqueeze(1))
                
                return {
                    'neural_output': neural_output,
                    'symbolic_output': symbolic_output,
                    'combined_output': combined_output
                }
            else:
                return {
                    'neural_output': neural_output,
                    'combined_output': neural_output
                }
                
        elif self.integration_mode == 'interactive':
            neural_output = self.neural_model(x)
            
            if symbolic_queries is not None:
                informed_queries = self._inform_symbolic_queries(neural_output, symbolic_queries)
                symbolic_output = self.symbolic_reasoner.to_tensor(informed_queries)
                
                refined_output = self._refine_neural_output(neural_output, symbolic_output)
                
                return {
                    'neural_output': neural_output,
                    'symbolic_output': symbolic_output,
                    'combined_output': refined_output
                }
            else:
                return {
                    'neural_output': neural_output,
                    'combined_output': neural_output
                }
    
    def _inform_symbolic_queries(self, neural_output: Tensor, 
                               symbolic_queries: List[str]) -> List[str]:
        """Use neural output to inform symbolic queries.
        
        Args:
            neural_output: Output tensor from the neural network
            symbolic_queries: List of symbolic queries to evaluate
            
        Returns:
            List of informed symbolic queries
        """
        return symbolic_queries
    
    def _refine_neural_output(self, neural_output: Tensor, 
                            symbolic_output: Tensor) -> Tensor:
        """Use symbolic output to refine neural output.
        
        Args:
            neural_output: Output tensor from the neural network
            symbolic_output: Output tensor from symbolic reasoning
            
        Returns:
            Refined output tensor
        """
        return neural_output * (1.0 + symbolic_output.unsqueeze(1))


class AbductiveReasoning(Module):
    """Abductive reasoning for hybrid neuro-symbolic models."""
    
    def __init__(self, neural_model: Module, symbolic_reasoner: SymbolicReasoner,
                num_hypotheses: int = 5):
        """Initialize an abductive reasoning model.
        
        Args:
            neural_model: The neural network model
            symbolic_reasoner: The symbolic reasoner
            num_hypotheses: Number of hypotheses to generate
        """
        super().__init__()
        self.neural_model = neural_model
        self.symbolic_reasoner = symbolic_reasoner
        self.num_hypotheses = num_hypotheses
        
    def forward(self, x: Tensor) -> Dict[str, Any]:
        """Forward pass through the abductive reasoning model.
        
        Args:
            x: Input tensor
            
        Returns:
            Dictionary of abductive reasoning results
        """
        neural_output = self.neural_model(x)
        
        hypotheses = self._generate_hypotheses(neural_output)
        
        hypothesis_scores = self._evaluate_hypotheses(hypotheses, neural_output)
        
        best_hypothesis_idx = hypothesis_scores.argmax().item()
        best_hypothesis = hypotheses[best_hypothesis_idx]
        
        return {
            'neural_output': neural_output,
            'hypotheses': hypotheses,
            'hypothesis_scores': hypothesis_scores,
            'best_hypothesis': best_hypothesis
        }
    
    def _generate_hypotheses(self, neural_output: Tensor) -> List[str]:
        """Generate hypotheses based on neural output.
        
        Args:
            neural_output: Output tensor from the neural network
            
        Returns:
            List of hypothesis strings
        """
        return [f"hypothesis_{i}" for i in range(self.num_hypotheses)]
    
    def _evaluate_hypotheses(self, hypotheses: List[str], 
                           neural_output: Tensor) -> Tensor:
        """Evaluate hypotheses based on neural output.
        
        Args:
            hypotheses: List of hypothesis strings
            neural_output: Output tensor from the neural network
            
        Returns:
            Tensor of hypothesis scores
        """
        return Tensor.rand(len(hypotheses))


class DeductiveReasoning(Module):
    """Deductive reasoning for hybrid neuro-symbolic models."""
    
    def __init__(self, neural_model: Module, symbolic_reasoner: SymbolicReasoner):
        """Initialize a deductive reasoning model.
        
        Args:
            neural_model: The neural network model
            symbolic_reasoner: The symbolic reasoner
        """
        super().__init__()
        self.neural_model = neural_model
        self.symbolic_reasoner = symbolic_reasoner
        
    def forward(self, x: Tensor, premises: List[str]) -> Dict[str, Any]:
        """Forward pass through the deductive reasoning model.
        
        Args:
            x: Input tensor
            premises: List of premise strings
            
        Returns:
            Dictionary of deductive reasoning results
        """
        neural_output = self.neural_model(x)
        
        conclusions = self._generate_conclusions(neural_output, premises)
        
        conclusion_scores = self._evaluate_conclusions(conclusions, premises)
        
        best_conclusion_idx = conclusion_scores.argmax().item()
        best_conclusion = conclusions[best_conclusion_idx]
        
        return {
            'neural_output': neural_output,
            'premises': premises,
            'conclusions': conclusions,
            'conclusion_scores': conclusion_scores,
            'best_conclusion': best_conclusion
        }
    
    def _generate_conclusions(self, neural_output: Tensor, 
                            premises: List[str]) -> List[str]:
        """Generate conclusions based on neural output and premises.
        
        Args:
            neural_output: Output tensor from the neural network
            premises: List of premise strings
            
        Returns:
            List of conclusion strings
        """
        return [f"conclusion_{i}" for i in range(5)]
    
    def _evaluate_conclusions(self, conclusions: List[str], 
                            premises: List[str]) -> Tensor:
        """Evaluate conclusions based on premises.
        
        Args:
            conclusions: List of conclusion strings
            premises: List of premise strings
            
        Returns:
            Tensor of conclusion scores
        """
        return Tensor.rand(len(conclusions))


class InductiveReasoning(Module):
    """Inductive reasoning for hybrid neuro-symbolic models."""
    
    def __init__(self, neural_model: Module, symbolic_reasoner: SymbolicReasoner,
                num_rules: int = 5):
        """Initialize an inductive reasoning model.
        
        Args:
            neural_model: The neural network model
            symbolic_reasoner: The symbolic reasoner
            num_rules: Number of rules to induce
        """
        super().__init__()
        self.neural_model = neural_model
        self.symbolic_reasoner = symbolic_reasoner
        self.num_rules = num_rules
        
    def forward(self, x: Tensor, examples: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Forward pass through the inductive reasoning model.
        
        Args:
            x: Input tensor
            examples: List of example dictionaries
            
        Returns:
            Dictionary of inductive reasoning results
        """
        neural_output = self.neural_model(x)
        
        rules = self._induce_rules(neural_output, examples)
        
        rule_scores = self._evaluate_rules(rules, examples)
        
        best_rule_idx = rule_scores.argmax().item()
        best_rule = rules[best_rule_idx]
        
        return {
            'neural_output': neural_output,
            'examples': examples,
            'rules': rules,
            'rule_scores': rule_scores,
            'best_rule': best_rule
        }
    
    def _induce_rules(self, neural_output: Tensor, 
                    examples: List[Dict[str, Any]]) -> List[Tuple[str, List[str]]]:
        """Induce rules based on neural output and examples.
        
        Args:
            neural_output: Output tensor from the neural network
            examples: List of example dictionaries
            
        Returns:
            List of induced rules as (head, body) tuples
        """
        return [(f"head_{i}", [f"body_{i}_1", f"body_{i}_2"]) for i in range(self.num_rules)]
    
    def _evaluate_rules(self, rules: List[Tuple[str, List[str]]],
                      examples: List[Dict[str, Any]]) -> Tensor:
        """Evaluate rules based on examples.
        
        Args:
            rules: List of rules as (head, body) tuples
            examples: List of example dictionaries
            
        Returns:
            Tensor of rule scores
        """
        return Tensor.rand(len(rules))
