"""Neurosymbolic reasoning implementation."""

import logging
from typing import Dict, Any, List, Tuple
import json

from .base import ReasoningStrategy

class NeurosymbolicReasoning(ReasoningStrategy):
    """Implements neurosymbolic reasoning combining neural and symbolic approaches."""
    
    async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
        try:
            # Neural processing
            neural_features = await self._neural_processing(query, context)
            
            # Symbolic reasoning
            symbolic_rules = await self._symbolic_reasoning(neural_features, context)
            
            # Integration
            integrated = await self._neurosymbolic_integration(neural_features, symbolic_rules, context)
            
            # Final inference
            conclusion = await self._final_inference(integrated, context)
            
            return {
                "success": True,
                "answer": conclusion["answer"],
                "neural_features": neural_features,
                "symbolic_rules": symbolic_rules,
                "integrated_reasoning": integrated,
                "confidence": conclusion["confidence"],
                "explanation": conclusion["explanation"]
            }
        except Exception as e:
            return {"success": False, "error": str(e)}

    async def _neural_processing(self, query: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Extract neural features from query:
        Query: {query}
        Context: {json.dumps(context)}
        
        For each feature:
        1. [Type]: Feature type
        2. [Value]: Extracted value
        3. [Confidence]: Extraction confidence
        4. [Relations]: Related concepts
        
        Format as:
        [F1]
        Type: ...
        Value: ...
        Confidence: ...
        Relations: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_features(response["answer"])

    async def _symbolic_reasoning(self, features: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Generate symbolic rules from features:
        Features: {json.dumps(features)}
        Context: {json.dumps(context)}
        
        For each rule:
        1. [Condition]: Rule condition
        2. [Implication]: What it implies
        3. [Certainty]: Rule certainty
        4. [Source]: Derivation source
        
        Format as:
        [R1]
        Condition: ...
        Implication: ...
        Certainty: ...
        Source: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_rules(response["answer"])

    async def _neurosymbolic_integration(self, features: List[Dict[str, Any]], rules: List[Dict[str, Any]], context: Dict[str, Any]) -> List[Dict[str, Any]]:
        prompt = f"""
        Integrate neural and symbolic components:
        Features: {json.dumps(features)}
        Rules: {json.dumps(rules)}
        Context: {json.dumps(context)}
        
        For each integration:
        1. [Components]: What is being integrated
        2. [Method]: How they are combined
        3. [Result]: Integration outcome
        4. [Confidence]: Integration confidence
        
        Format as:
        [I1]
        Components: ...
        Method: ...
        Result: ...
        Confidence: ...
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_integration(response["answer"])

    async def _final_inference(self, integrated: List[Dict[str, Any]], context: Dict[str, Any]) -> Dict[str, Any]:
        prompt = f"""
        Draw final conclusions from integrated reasoning:
        Integrated: {json.dumps(integrated)}
        Context: {json.dumps(context)}
        
        Provide:
        1. Final answer/conclusion
        2. Confidence level (0-1)
        3. Explanation of reasoning
        4. Key factors considered
        """
        
        response = await context["groq_api"].predict(prompt)
        return self._parse_conclusion(response["answer"])

    def _parse_features(self, response: str) -> List[Dict[str, Any]]:
        """Parse neural features from response."""
        features = []
        current = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[F'):
                if current:
                    features.append(current)
                current = {
                    "type": "",
                    "value": "",
                    "confidence": 0.0,
                    "relations": []
                }
            elif current:
                if line.startswith('Type:'):
                    current["type"] = line[5:].strip()
                elif line.startswith('Value:'):
                    current["value"] = line[6:].strip()
                elif line.startswith('Confidence:'):
                    try:
                        current["confidence"] = float(line[11:].strip())
                    except:
                        pass
                elif line.startswith('Relations:'):
                    current["relations"] = [r.strip() for r in line[10:].split(',')]
        
        if current:
            features.append(current)
        
        return features

    def _parse_rules(self, response: str) -> List[Dict[str, Any]]:
        """Parse symbolic rules from response."""
        rules = []
        current = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[R'):
                if current:
                    rules.append(current)
                current = {
                    "condition": "",
                    "implication": "",
                    "certainty": 0.0,
                    "source": ""
                }
            elif current:
                if line.startswith('Condition:'):
                    current["condition"] = line[10:].strip()
                elif line.startswith('Implication:'):
                    current["implication"] = line[12:].strip()
                elif line.startswith('Certainty:'):
                    try:
                        current["certainty"] = float(line[10:].strip())
                    except:
                        pass
                elif line.startswith('Source:'):
                    current["source"] = line[7:].strip()
        
        if current:
            rules.append(current)
        
        return rules

    def _parse_integration(self, response: str) -> List[Dict[str, Any]]:
        """Parse integration results from response."""
        integrations = []
        current = None
        
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('[I'):
                if current:
                    integrations.append(current)
                current = {
                    "components": "",
                    "method": "",
                    "result": "",
                    "confidence": 0.0
                }
            elif current:
                if line.startswith('Components:'):
                    current["components"] = line[11:].strip()
                elif line.startswith('Method:'):
                    current["method"] = line[7:].strip()
                elif line.startswith('Result:'):
                    current["result"] = line[7:].strip()
                elif line.startswith('Confidence:'):
                    try:
                        current["confidence"] = float(line[11:].strip())
                    except:
                        pass
        
        if current:
            integrations.append(current)
        
        return integrations

    def _parse_conclusion(self, response: str) -> Dict[str, Any]:
        """Parse final conclusion from response."""
        conclusion = {
            "answer": "",
            "confidence": 0.0,
            "explanation": "",
            "factors": []
        }
        
        mode = None
        for line in response.split('\n'):
            line = line.strip()
            if not line:
                continue
                
            if line.startswith('Answer:'):
                conclusion["answer"] = line[7:].strip()
            elif line.startswith('Confidence:'):
                try:
                    conclusion["confidence"] = float(line[11:].strip())
                except:
                    conclusion["confidence"] = 0.5
            elif line.startswith('Explanation:'):
                conclusion["explanation"] = line[12:].strip()
            elif line.startswith('Factors:'):
                mode = "factors"
            elif mode == "factors" and line.startswith('- '):
                conclusion["factors"].append(line[2:].strip())
        
        return conclusion