"""Analogical reasoning implementation with advanced pattern matching and transfer learning."""

import logging
from typing import Dict, Any, List, Optional, Set, Tuple, Callable
import json
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime
import numpy as np
from collections import defaultdict

from .base import ReasoningStrategy

class AnalogicalLevel(Enum):
    """Levels of analogical similarity."""
    SURFACE = "surface"
    STRUCTURAL = "structural"
    SEMANTIC = "semantic"
    FUNCTIONAL = "functional"
    CAUSAL = "causal"
    ABSTRACT = "abstract"

class MappingType(Enum):
    """Types of analogical mappings."""
    DIRECT = "direct"
    TRANSFORMED = "transformed"
    COMPOSITE = "composite"
    ABSTRACT = "abstract"
    METAPHORICAL = "metaphorical"
    HYBRID = "hybrid"

@dataclass
class AnalogicalPattern:
    """Represents a pattern for analogical matching."""
    id: str
    level: AnalogicalLevel
    features: Dict[str, Any]
    relations: List[Tuple[str, str, str]]  # (entity1, relation, entity2)
    constraints: List[str]
    metadata: Dict[str, Any] = field(default_factory=dict)

@dataclass
class AnalogicalMapping:
    """Represents a mapping between source and target domains."""
    id: str
    type: MappingType
    source_elements: Dict[str, Any]
    target_elements: Dict[str, Any]
    correspondences: List[Tuple[str, str, float]]  # (source, target, strength)
    transformations: List[Dict[str, Any]]
    confidence: float
    metadata: Dict[str, Any] = field(default_factory=dict)

@dataclass
class AnalogicalSolution:
    """Represents a solution derived through analogical reasoning."""
    id: str
    source_analogy: str
    mapping: AnalogicalMapping
    adaptation: Dict[str, Any]
    inference: Dict[str, Any]
    confidence: float
    validation: Dict[str, Any]
    metadata: Dict[str, Any] = field(default_factory=dict)

class AnalogicalReasoning(ReasoningStrategy):
    """
    Advanced Analogical Reasoning implementation with:
    - Multi-level pattern matching
    - Sophisticated similarity metrics
    - Transfer learning capabilities
    - Dynamic adaptation mechanisms
    - Quality assessment
    - Learning from experience
    """
    
    def __init__(self, config: Optional[Dict[str, Any]] = None):
        """Initialize analogical reasoning."""
        super().__init__()
        self.config = config or {}
        
        # Standard reasoning parameters
        self.min_confidence = self.config.get('min_confidence', 0.7)
        self.parallel_threshold = self.config.get('parallel_threshold', 3)
        self.learning_rate = self.config.get('learning_rate', 0.1)
        self.strategy_weights = self.config.get('strategy_weights', {
            "LOCAL_LLM": 0.8,
            "CHAIN_OF_THOUGHT": 0.6,
            "TREE_OF_THOUGHTS": 0.5,
            "META_LEARNING": 0.4
        })
        
        # Analogical reasoning specific parameters
        self.min_similarity = self.config.get('min_similarity', 0.6)
        self.max_candidates = self.config.get('max_candidates', 5)
        self.adaptation_threshold = self.config.get('adaptation_threshold', 0.7)
        
        # Knowledge base
        self.patterns: Dict[str, AnalogicalPattern] = {}
        self.mappings: Dict[str, AnalogicalMapping] = {}
        self.solutions: Dict[str, AnalogicalSolution] = {}
        
        # Learning components
        self.pattern_weights: Dict[str, float] = defaultdict(float)
        self.success_history: List[Dict[str, Any]] = []
        self.adaptation_history: List[Dict[str, Any]] = []
        
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        """Main reasoning method implementing analogical reasoning."""
        try:
            # Extract patterns from query
            patterns = await self._extract_patterns(query, context)
            
            # Find analogical matches
            matches = await self._find_matches(patterns, context)
            
            # Create and evaluate mappings
            mappings = await self._create_mappings(matches, context)
            
            # Generate and adapt solutions
            solutions = await self._generate_solutions(mappings, context)
            
            # Select best solution
            best_solution = await self._select_best_solution(solutions, context)
            
            # Learn from experience
            self._update_knowledge(patterns, mappings, best_solution)
            
            return {
                "success": True,
                "answer": best_solution.inference["conclusion"],
                "confidence": best_solution.confidence,
                "analogy": {
                    "source": best_solution.source_analogy,
                    "mapping": self._mapping_to_dict(best_solution.mapping),
                    "adaptation": best_solution.adaptation
                },
                "reasoning_trace": best_solution.metadata.get("reasoning_trace", []),
                "meta_insights": best_solution.metadata.get("meta_insights", [])
            }
        except Exception as e:
            logging.error(f"Error in analogical reasoning: {str(e)}")
            return {"success": False, "error": str(e)}

    async def _extract_patterns(self, query: str, context: Dict[str, Any]) -> List[AnalogicalPattern]:
        """Extract patterns from query for analogical matching."""
        prompt = f"""
        Extract analogical patterns from query:
        Query: {query}
        Context: {json.dumps(context)}
        
        For each pattern level:
        1. Surface features
        2. Structural relations
        3. Semantic concepts
        4. Functional roles
        5. Causal relationships
        6. Abstract principles
        
        Format as:
        [P1]
        Level: ...
        Features: ...
        Relations: ...
        Constraints: ...
        
        [P2]
        ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_patterns(response["answer"])

    async def _find_matches(self, patterns: List[AnalogicalPattern], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Find matching patterns in knowledge base."""
        prompt = f"""
        Find analogical matches:
        Patterns: {json.dumps([self._pattern_to_dict(p) for p in patterns])}
        Context: {json.dumps(context)}
        
        For each match provide:
        1. Source domain
        2. Similarity assessment
        3. Key correspondences
        4. Transfer potential
        
        Format as:
        [M1]
        Source: ...
        Similarity: ...
        Correspondences: ...
        Transfer: ...
        
        [M2]
        ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_matches(response["answer"])

    async def _create_mappings(self, matches: List[Dict[str, Any]], context: Dict[str, Any]) -> List[AnalogicalMapping]:
        """Create mappings between source and target domains."""
        prompt = f"""
        Create analogical mappings:
        Matches: {json.dumps(matches)}
        Context: {json.dumps(context)}
        
        For each mapping specify:
        1. [Type]: {" | ".join([t.value for t in MappingType])}
        2. [Elements]: Source and target elements
        3. [Correspondences]: Element mappings
        4. [Transformations]: Required adaptations
        5. [Confidence]: Mapping strength
        
        Format as:
        [Map1]
        Type: ...
        Elements: ...
        Correspondences: ...
        Transformations: ...
        Confidence: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_mappings(response["answer"])

    async def _generate_solutions(self, mappings: List[AnalogicalMapping], context: Dict[str, Any]) -> List[AnalogicalSolution]:
        """Generate solutions through analogical transfer."""
        prompt = f"""
        Generate analogical solutions:
        Mappings: {json.dumps([self._mapping_to_dict(m) for m in mappings])}
        Context: {json.dumps(context)}
        
        For each solution provide:
        1. Analogical inference
        2. Required adaptations
        3. Validation criteria
        4. Confidence assessment
        5. Reasoning trace
        
        Format as:
        [S1]
        Inference: ...
        Adaptation: ...
        Validation: ...
        Confidence: ...
        Trace: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_solutions(response["answer"], mappings)

    async def _select_best_solution(self, solutions: List[AnalogicalSolution], context: Dict[str, Any]) -> AnalogicalSolution:
        """Select the best solution based on multiple criteria."""
        prompt = f"""
        Evaluate and select best solution:
        Solutions: {json.dumps([self._solution_to_dict(s) for s in solutions])}
        Context: {json.dumps(context)}
        
        Evaluate based on:
        1. Inference quality
        2. Adaptation feasibility
        3. Validation strength
        4. Overall confidence
        
        Format as:
        [Evaluation]
        Rankings: ...
        Rationale: ...
        Selection: ...
        Confidence: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        selection = self._parse_selection(response["answer"])
        
        # Find selected solution
        selected = max(solutions, key=lambda s: s.confidence)
        for solution in solutions:
            if solution.id == selection.get("selected_id"):
                selected = solution
                break
        
        return selected

    def _update_knowledge(self, patterns: List[AnalogicalPattern], mappings: List[AnalogicalMapping], solution: AnalogicalSolution):
        """Update knowledge base with new patterns and successful mappings."""
        # Update patterns
        for pattern in patterns:
            if pattern.id not in self.patterns:
                self.patterns[pattern.id] = pattern
            self.pattern_weights[pattern.id] += self.learning_rate * solution.confidence
        
        # Update mappings
        if solution.mapping.id not in self.mappings:
            self.mappings[solution.mapping.id] = solution.mapping
        
        # Record solution
        self.solutions[solution.id] = solution
        
        # Update history
        self.success_history.append({
            "timestamp": datetime.now().isoformat(),
            "solution_id": solution.id,
            "confidence": solution.confidence,
            "patterns": [p.id for p in patterns],
            "mapping_type": solution.mapping.type.value
        })
        
        # Update adaptation history
        self.adaptation_history.append({
            "timestamp": datetime.now().isoformat(),
            "solution_id": solution.id,
            "adaptations": solution.adaptation,
            "success": solution.confidence >= self.adaptation_threshold
        })

    def _parse_patterns(self, response: str) -> List[AnalogicalPattern]:
        """Parse patterns from response."""
        patterns = []
        current = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[P'):
                if current:
                    patterns.append(current)
                current = None
            elif line.startswith('Level:'):
                level_str = line[6:].strip().lower()
                try:
                    level = AnalogicalLevel(level_str)
                    current = AnalogicalPattern(
                        id=f"pattern_{len(patterns)}",
                        level=level,
                        features={},
                        relations=[],
                        constraints=[],
                        metadata={}
                    )
                except ValueError:
                    logging.warning(f"Invalid analogical level: {level_str}")
            elif current:
                if line.startswith('Features:'):
                    try:
                        current.features = json.loads(line[9:].strip())
                    except:
                        current.features = {"raw": line[9:].strip()}
                elif line.startswith('Relations:'):
                    relations = [r.strip() for r in line[10:].split(',')]
                    current.relations = [(r.split()[0], r.split()[1], r.split()[2]) 
                                      for r in relations if len(r.split()) >= 3]
                elif line.startswith('Constraints:'):
                    current.constraints = [c.strip() for c in line[12:].split(',')]
        
        if current:
            patterns.append(current)
        
        return patterns

    def _parse_matches(self, response: str) -> List[Dict[str, Any]]:
        """Parse matches from response."""
        matches = []
        current = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[M'):
                if current:
                    matches.append(current)
                current = {
                    "source": "",
                    "similarity": 0.0,
                    "correspondences": [],
                    "transfer": []
                }
            elif current:
                if line.startswith('Source:'):
                    current["source"] = line[7:].strip()
                elif line.startswith('Similarity:'):
                    try:
                        current["similarity"] = float(line[11:].strip())
                    except:
                        pass
                elif line.startswith('Correspondences:'):
                    current["correspondences"] = [c.strip() for c in line[16:].split(',')]
                elif line.startswith('Transfer:'):
                    current["transfer"] = [t.strip() for t in line[9:].split(',')]
        
        if current:
            matches.append(current)
        
        return matches

    def _parse_mappings(self, response: str) -> List[AnalogicalMapping]:
        """Parse mappings from response."""
        mappings = []
        current = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[Map'):
                if current:
                    mappings.append(current)
                current = None
            elif line.startswith('Type:'):
                type_str = line[5:].strip().lower()
                try:
                    mapping_type = MappingType(type_str)
                    current = AnalogicalMapping(
                        id=f"mapping_{len(mappings)}",
                        type=mapping_type,
                        source_elements={},
                        target_elements={},
                        correspondences=[],
                        transformations=[],
                        confidence=0.0,
                        metadata={}
                    )
                except ValueError:
                    logging.warning(f"Invalid mapping type: {type_str}")
            elif current:
                if line.startswith('Elements:'):
                    try:
                        elements = json.loads(line[9:].strip())
                        current.source_elements = elements.get("source", {})
                        current.target_elements = elements.get("target", {})
                    except:
                        pass
                elif line.startswith('Correspondences:'):
                    pairs = [c.strip() for c in line[16:].split(',')]
                    for pair in pairs:
                        parts = pair.split(':')
                        if len(parts) >= 2:
                            source = parts[0].strip()
                            target = parts[1].strip()
                            strength = float(parts[2]) if len(parts) > 2 else 1.0
                            current.correspondences.append((source, target, strength))
                elif line.startswith('Transformations:'):
                    try:
                        current.transformations = json.loads(line[16:].strip())
                    except:
                        current.transformations = [{"raw": line[16:].strip()}]
                elif line.startswith('Confidence:'):
                    try:
                        current.confidence = float(line[11:].strip())
                    except:
                        pass
        
        if current:
            mappings.append(current)
        
        return mappings

    def _parse_solutions(self, response: str, mappings: List[AnalogicalMapping]) -> List[AnalogicalSolution]:
        """Parse solutions from response."""
        solutions = []
        current = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[S'):
                if current:
                    solutions.append(current)
                current = None
                mapping_idx = len(solutions)
                if mapping_idx < len(mappings):
                    current = AnalogicalSolution(
                        id=f"solution_{len(solutions)}",
                        source_analogy="",
                        mapping=mappings[mapping_idx],
                        adaptation={},
                        inference={},
                        confidence=0.0,
                        validation={},
                        metadata={}
                    )
            elif current:
                if line.startswith('Inference:'):
                    try:
                        current.inference = json.loads(line[10:].strip())
                    except:
                        current.inference = {"conclusion": line[10:].strip()}
                elif line.startswith('Adaptation:'):
                    try:
                        current.adaptation = json.loads(line[11:].strip())
                    except:
                        current.adaptation = {"steps": [line[11:].strip()]}
                elif line.startswith('Validation:'):
                    try:
                        current.validation = json.loads(line[11:].strip())
                    except:
                        current.validation = {"criteria": [line[11:].strip()]}
                elif line.startswith('Confidence:'):
                    try:
                        current.confidence = float(line[11:].strip())
                    except:
                        pass
                elif line.startswith('Trace:'):
                    current.metadata["reasoning_trace"] = [t.strip() for t in line[6:].split(',')]
        
        if current:
            solutions.append(current)
        
        return solutions

    def _parse_selection(self, response: str) -> Dict[str, Any]:
        """Parse solution selection from response."""
        selection = {
            "selected_id": None,
            "confidence": 0.0,
            "rationale": []
        }
        
        for line in response.split('\n'):
            line = line.strip()
            if line.startswith('Selection:'):
                selection["selected_id"] = line[10:].strip()
            elif line.startswith('Confidence:'):
                try:
                    selection["confidence"] = float(line[11:].strip())
                except:
                    pass
            elif line.startswith('Rationale:'):
                selection["rationale"] = [r.strip() for r in line[10:].split(',')]
        
        return selection

    def _pattern_to_dict(self, pattern: AnalogicalPattern) -> Dict[str, Any]:
        """Convert pattern to dictionary for serialization."""
        return {
            "id": pattern.id,
            "level": pattern.level.value,
            "features": pattern.features,
            "relations": pattern.relations,
            "constraints": pattern.constraints,
            "metadata": pattern.metadata
        }

    def _mapping_to_dict(self, mapping: AnalogicalMapping) -> Dict[str, Any]:
        """Convert mapping to dictionary for serialization."""
        return {
            "id": mapping.id,
            "type": mapping.type.value,
            "source_elements": mapping.source_elements,
            "target_elements": mapping.target_elements,
            "correspondences": mapping.correspondences,
            "transformations": mapping.transformations,
            "confidence": mapping.confidence,
            "metadata": mapping.metadata
        }

    def _solution_to_dict(self, solution: AnalogicalSolution) -> Dict[str, Any]:
        """Convert solution to dictionary for serialization."""
        return {
            "id": solution.id,
            "source_analogy": solution.source_analogy,
            "mapping": self._mapping_to_dict(solution.mapping),
            "adaptation": solution.adaptation,
            "inference": solution.inference,
            "confidence": solution.confidence,
            "validation": solution.validation,
            "metadata": solution.metadata
        }

    def get_pattern_statistics(self) -> Dict[str, Any]:
        """Get statistics about pattern usage and effectiveness."""
        return {
            "total_patterns": len(self.patterns),
            "level_distribution": defaultdict(int, {p.level.value: 1 for p in self.patterns.values()}),
            "average_constraints": sum(len(p.constraints) for p in self.patterns.values()) / len(self.patterns) if self.patterns else 0,
            "pattern_weights": dict(self.pattern_weights)
        }

    def get_mapping_statistics(self) -> Dict[str, Any]:
        """Get statistics about mapping effectiveness."""
        return {
            "total_mappings": len(self.mappings),
            "type_distribution": defaultdict(int, {m.type.value: 1 for m in self.mappings.values()}),
            "average_confidence": sum(m.confidence for m in self.mappings.values()) / len(self.mappings) if self.mappings else 0,
            "transformation_counts": defaultdict(int, {m.id: len(m.transformations) for m in self.mappings.values()})
        }

    def get_solution_statistics(self) -> Dict[str, Any]:
        """Get statistics about solution quality."""
        return {
            "total_solutions": len(self.solutions),
            "average_confidence": sum(s.confidence for s in self.solutions.values()) / len(self.solutions) if self.solutions else 0,
            "adaptation_success_rate": sum(1 for h in self.adaptation_history if h["success"]) / len(self.adaptation_history) if self.adaptation_history else 0
        }

    def clear_knowledge_base(self):
        """Clear the knowledge base."""
        self.patterns.clear()
        self.mappings.clear()
        self.solutions.clear()
        self.pattern_weights.clear()
        self.success_history.clear()
        self.adaptation_history.clear()