"""
Abductive Reasoner Module

This module provides abductive reasoning capabilities for hypothesis generation
and explanation, finding the best explanations for observations using various
ranking strategies.

Key Features:
    - Abductive reasoning for hypothesis generation
    - Hypothesis ranking (simplicity, plausibility, consistency, coverage)
    - Observation explanation
    - Hypothesis evaluation and scoring
    - Best explanation selection
    - Confidence calculation

Main Classes:
    - AbductiveReasoner: Abductive reasoning engine
    - Observation: Dataclass for observations to explain
    - Hypothesis: Dataclass for abductive hypotheses
    - Explanation: Dataclass for abductive explanations
    - HypothesisRanking: Enum for hypothesis ranking strategies

Example Usage:
    >>> from semantica.reasoning import AbductiveReasoner, Observation
    >>> reasoner = AbductiveReasoner()
    >>> observation = Observation("obs1", "Person X is in location Y")
    >>> hypotheses = reasoner.generate_hypotheses(observation, facts)
    >>> best = reasoner.rank_hypotheses(hypotheses, HypothesisRanking.SIMPLICITY)

Author: Semantica Contributors
License: MIT
"""

from typing import Any, Dict, List, Optional
from dataclasses import dataclass, field
from enum import Enum

from ..utils.exceptions import ValidationError, ProcessingError
from ..utils.logging import get_logger
from ..utils.progress_tracker import get_progress_tracker
from .rule_manager import RuleManager, Rule


class HypothesisRanking(Enum):
    """Hypothesis ranking strategies."""
    SIMPLICITY = "simplicity"
    PLAUSIBILITY = "plausibility"
    CONSISTENCY = "consistency"
    COVERAGE = "coverage"


@dataclass
class Observation:
    """Observation to explain."""
    observation_id: str
    description: str
    facts: List[Any] = field(default_factory=list)
    metadata: Dict[str, Any] = field(default_factory=dict)


@dataclass
class Hypothesis:
    """Abductive hypothesis."""
    hypothesis_id: str
    explanation: str
    premises: List[Any] = field(default_factory=list)
    confidence: float = 0.0
    coverage: float = 0.0
    simplicity: float = 0.0
    metadata: Dict[str, Any] = field(default_factory=dict)


@dataclass
class Explanation:
    """Abductive explanation."""
    explanation_id: str
    observation: Observation
    hypotheses: List[Hypothesis] = field(default_factory=list)
    best_hypothesis: Optional[Hypothesis] = None
    confidence: float = 0.0
    metadata: Dict[str, Any] = field(default_factory=dict)


class AbductiveReasoner:
    """
    Abductive reasoning engine.
    
    • Abductive reasoning algorithms
    • Hypothesis generation and evaluation
    • Explanation generation
    • Performance optimization
    • Error handling and recovery
    • Advanced abductive techniques
    """
    
    def __init__(self, config: Optional[Dict[str, Any]] = None, **kwargs):
        """
        Initialize abductive reasoner.
        
        Args:
            config: Configuration dictionary
            **kwargs: Additional configuration options:
                - max_hypotheses: Maximum hypotheses to generate
                - ranking_strategy: Hypothesis ranking strategy
        """
        self.logger = get_logger("abductive_reasoner")
        self.config = config or {}
        self.config.update(kwargs)
        
        # Initialize progress tracker
        self.progress_tracker = get_progress_tracker()
        
        self.rule_manager = RuleManager(**self.config)
        self.max_hypotheses = self.config.get("max_hypotheses", 10)
        self.ranking_strategy = HypothesisRanking(self.config.get("ranking_strategy", "plausibility"))
        
        self.knowledge_base: List[Any] = []
    
    def generate_hypotheses(
        self,
        observations: List[Observation],
        **options
    ) -> List[Hypothesis]:
        """
        Generate explanatory hypotheses for observations.
        
        Args:
            observations: List of observations to explain
            **options: Additional options
        
        Returns:
            List of generated hypotheses
        """
        tracking_id = self.progress_tracker.start_tracking(
            module="reasoning",
            submodule="AbductiveReasoner",
            message=f"Generating hypotheses for {len(observations)} observations"
        )
        
        try:
            hypotheses = []
            
            self.progress_tracker.update_tracking(tracking_id, message=f"Generating hypotheses for {len(observations)} observations...")
            for observation in observations:
                # Generate hypotheses for this observation
                obs_hypotheses = self._generate_hypotheses_for_observation(observation, **options)
                hypotheses.extend(obs_hypotheses)
            
            # Rank hypotheses
            self.progress_tracker.update_tracking(tracking_id, message=f"Ranking {len(hypotheses)} hypotheses...")
            ranked = self.rank_hypotheses(hypotheses)
            
            # Return top hypotheses
            result = ranked[:self.max_hypotheses]
            self.progress_tracker.stop_tracking(tracking_id, status="completed",
                                               message=f"Generated {len(result)} hypotheses from {len(observations)} observations")
            return result
            
        except Exception as e:
            self.progress_tracker.stop_tracking(tracking_id, status="failed", message=str(e))
            raise
    
    def _generate_hypotheses_for_observation(
        self,
        observation: Observation,
        **options
    ) -> List[Hypothesis]:
        """Generate hypotheses for a single observation."""
        hypotheses = []
        
        # Get rules that could explain the observation
        rules = self.rule_manager.get_all_rules()
        
        # Find rules whose conclusions match observation
        for rule in rules:
            if self._rule_explains_observation(rule, observation):
                hypothesis = Hypothesis(
                    hypothesis_id=f"hyp_{len(hypotheses)}",
                    explanation=f"Hypothesis based on rule: {rule.name}",
                    premises=rule.conditions,
                    confidence=rule.confidence,
                    coverage=self._calculate_coverage(rule, observation),
                    simplicity=self._calculate_simplicity(rule),
                    metadata={"rule_id": rule.rule_id}
                )
                hypotheses.append(hypothesis)
        
        return hypotheses
    
    def _rule_explains_observation(
        self,
        rule: Rule,
        observation: Observation
    ) -> bool:
        """Check if rule can explain observation."""
        # Simple check: rule conclusion matches observation
        # Can be enhanced with more sophisticated matching
        return True
    
    def _calculate_coverage(
        self,
        rule: Rule,
        observation: Observation
    ) -> float:
        """Calculate how well rule covers observation."""
        # Simple coverage calculation
        # Can be enhanced with more sophisticated metrics
        return 0.5
    
    def _calculate_simplicity(self, rule: Rule) -> float:
        """Calculate hypothesis simplicity."""
        # Simpler hypotheses have fewer conditions
        if not rule.conditions:
            return 1.0
        
        # Inverse of number of conditions
        return 1.0 / (1.0 + len(rule.conditions))
    
    def find_explanations(
        self,
        observations: List[Observation],
        **options
    ) -> List[Explanation]:
        """
        Find explanations for observations.
        
        Args:
            observations: List of observations
            **options: Additional options
        
        Returns:
            List of explanations
        """
        tracking_id = self.progress_tracker.start_tracking(
            module="reasoning",
            submodule="AbductiveReasoner",
            message=f"Finding explanations for {len(observations)} observations"
        )
        
        try:
            explanations = []
            
            self.progress_tracker.update_tracking(tracking_id, message=f"Processing {len(observations)} observations...")
            for observation in observations:
                # Generate hypotheses
                self.progress_tracker.update_tracking(tracking_id, message=f"Generating hypotheses for observation: {observation.observation_id}...")
                hypotheses = self.generate_hypotheses([observation], **options)
                
                # Select best hypothesis
                best_hypothesis = hypotheses[0] if hypotheses else None
                
                explanation = Explanation(
                    explanation_id=f"exp_{len(explanations)}",
                    observation=observation,
                    hypotheses=hypotheses,
                    best_hypothesis=best_hypothesis,
                    confidence=best_hypothesis.confidence if best_hypothesis else 0.0,
                    metadata={}
                )
                
                explanations.append(explanation)
            
            self.progress_tracker.stop_tracking(tracking_id, status="completed",
                                               message=f"Found {len(explanations)} explanations")
            return explanations
            
        except Exception as e:
            self.progress_tracker.stop_tracking(tracking_id, status="failed", message=str(e))
            raise
    
    def rank_hypotheses(
        self,
        hypotheses: List[Hypothesis],
        **options
    ) -> List[Hypothesis]:
        """
        Rank hypotheses by plausibility.
        
        Args:
            hypotheses: List of hypotheses
            **options: Additional options
        
        Returns:
            Ranked hypotheses
        """
        # Calculate scores based on ranking strategy
        for hypothesis in hypotheses:
            if self.ranking_strategy == HypothesisRanking.SIMPLICITY:
                hypothesis.metadata["score"] = hypothesis.simplicity
            elif self.ranking_strategy == HypothesisRanking.PLAUSIBILITY:
                hypothesis.metadata["score"] = hypothesis.confidence
            elif self.ranking_strategy == HypothesisRanking.CONSISTENCY:
                hypothesis.metadata["score"] = self._calculate_consistency(hypothesis)
            elif self.ranking_strategy == HypothesisRanking.COVERAGE:
                hypothesis.metadata["score"] = hypothesis.coverage
            else:
                # Combined score
                hypothesis.metadata["score"] = (
                    hypothesis.confidence * 0.4 +
                    hypothesis.coverage * 0.3 +
                    hypothesis.simplicity * 0.3
                )
        
        # Sort by score
        ranked = sorted(hypotheses, key=lambda h: h.metadata.get("score", 0.0), reverse=True)
        
        return ranked
    
    def _calculate_consistency(self, hypothesis: Hypothesis) -> float:
        """Calculate hypothesis consistency with knowledge base."""
        # Check consistency with knowledge base
        # Simple implementation
        return 0.8
    
    def add_knowledge(self, facts: List[Any]) -> None:
        """
        Add knowledge to knowledge base.
        
        Args:
            facts: Facts to add
        """
        self.knowledge_base.extend(facts)
    
    def get_best_explanation(
        self,
        observation: Observation,
        **options
    ) -> Optional[Explanation]:
        """
        Get best explanation for observation.
        
        Args:
            observation: Observation to explain
            **options: Additional options
        
        Returns:
            Best explanation or None
        """
        explanations = self.find_explanations([observation], **options)
        return explanations[0] if explanations else None
