from ..base import ResearchTechnique
from typing import Dict, Any, List
import asyncio
from rich.progress import track
from dataclasses import dataclass

@dataclass
class MetaPromptStep:
    """Represents a step in meta-prompting process"""
    step_type: str
    prompt: str
    response: str
    confidence: float

class MetaPromptingEngine(ResearchTechnique):
    """
    Meta-Prompting: Enhancing Language Models with Task-Agnostic Scaffolding
    Implementation of advanced meta-prompting techniques
    """
    
    def __init__(self):
        super().__init__()
        self.name = " Meta-Prompting Engine"
        self.steps: List[MetaPromptStep] = []
        
    async def execute(self, request: Dict[str, Any]) -> Dict[str, Any]:
        """Execute meta-prompting workflow"""
        
        problem = request.get("problem", "")
        context = request.get("context", "")
        
        # Phase 1: Problem Decomposition
        decomposition = await self._decompose_problem(problem, context)
        
        # Phase 2: Strategy Selection
        strategy = await self._select_strategy(decomposition)
        
        # Phase 3: Multi-step Reasoning
        reasoning_chain = await self._execute_reasoning_chain(strategy)
        
        # Phase 4: Solution Synthesis
        final_solution = await self._synthesize_solution(reasoning_chain)
        
        # Phase 5: Self-Verification
        verification = await self._verify_solution(final_solution, problem)
        
        return {
            "solution": final_solution,
            "confidence": verification["confidence"],
            "reasoning_steps": len(self.steps),
            "meta_analysis": verification["analysis"],
            "visualization": self._generate_flow_chart()
        }
    
    async def _decompose_problem(self, problem: str, context: str) -> Dict[str, Any]:
        """Decompose complex problem into sub-problems"""
        
        meta_prompt = f"""
        As a meta-cognitive AI, analyze this problem systematically:
        
        Problem: {problem}
        Context: {context}
        
        Decompose into:
        1. Core components
        2. Dependencies
        3. Success criteria
        4. Potential approaches
        
        Format as structured analysis.
        """
        
        response = await self.llm_call(meta_prompt)
        
        step = MetaPromptStep(
            step_type="decomposition",
            prompt=meta_prompt,
            response=response,
            confidence=0.85
        )
        self.steps.append(step)
        
        return {"decomposition": response, "components": self._extract_components(response)}
    
    async def _select_strategy(self, decomposition: Dict[str, Any]) -> str:
        """Select optimal reasoning strategy"""
        
        strategies = [
            "Chain-of-Thought reasoning",
            "Tree-of-Thoughts exploration", 
            "Debate-driven analysis",
            "Step-by-step verification",
            "Analogical reasoning"
        ]
        
        strategy_prompt = f"""
        Given this problem decomposition:
        {decomposition['decomposition']}
        
        Select the most effective reasoning strategy from:
        {', '.join(strategies)}
        
        Explain your choice and outline the approach.
        """
        
        response = await self.llm_call(strategy_prompt)
        
        step = MetaPromptStep(
            step_type="strategy_selection",
            prompt=strategy_prompt,
            response=response,
            confidence=0.80
        )
        self.steps.append(step)
        
        return response
    
    async def _execute_reasoning_chain(self, strategy: str) -> List[str]:
        """Execute multi-step reasoning chain"""
        
        reasoning_steps = []
        
        for i in track(range(3), description="Executing reasoning chain..."):
            step_prompt = f"""
            Using {strategy}, execute reasoning step {i+1}:
            
            Previous steps: {reasoning_steps}
            
            Continue the logical progression.
            """
            
            response = await self.llm_call(step_prompt)
            reasoning_steps.append(response)
            
            step = MetaPromptStep(
                step_type=f"reasoning_step_{i+1}",
                prompt=step_prompt,
                response=response,
                confidence=0.75 + (i * 0.05)
            )
            self.steps.append(step)
        
        return reasoning_steps
    
    async def _synthesize_solution(self, reasoning_chain: List[str]) -> str:
        """Synthesize final solution from reasoning chain"""
        
        synthesis_prompt = f"""
        Synthesize a comprehensive solution from this reasoning chain:
        
        {chr(10).join(f"Step {i+1}: {step}" for i, step in enumerate(reasoning_chain))}
        
        Provide a clear, actionable solution.
        """
        
        response = await self.llm_call(synthesis_prompt)
        
        step = MetaPromptStep(
            step_type="synthesis",
            prompt=synthesis_prompt,
            response=response,
            confidence=0.90
        )
        self.steps.append(step)
        
        return response
    
    async def _verify_solution(self, solution: str, original_problem: str) -> Dict[str, Any]:
        """Self-verification of solution quality"""
        
        verification_prompt = f"""
        Verify this solution against the original problem:
        
        Original Problem: {original_problem}
        Proposed Solution: {solution}
        
        Analyze:
        1. Completeness (0-1 score)
        2. Accuracy (0-1 score) 
        3. Feasibility (0-1 score)
        4. Areas for improvement
        
        Provide numerical confidence score and detailed analysis.
        """
        
        response = await self.llm_call(verification_prompt)
        
        step = MetaPromptStep(
            step_type="verification",
            prompt=verification_prompt,
            response=response,
            confidence=0.85
        )
        self.steps.append(step)
        
        # Extract confidence score (simplified extraction)
        confidence = self._extract_confidence(response)
        
        return {
            "analysis": response,
            "confidence": confidence
        }
    
    def _generate_flow_chart(self) -> str:
        """Generate ASCII flow chart of reasoning process"""
        
        chart = "\n🔄 Meta-Prompting Flow:\n"
        chart += "┌─────────────────┐\n"
        chart += "│ Problem Input   │\n"
        chart += "└─────────┬───────┘\n"
        chart += "          │\n"
        chart += "┌─────────▼───────┐\n"
        chart += "│ Decomposition   │\n"
        chart += "└─────────┬───────┘\n"
        chart += "          │\n"
        chart += "┌─────────▼───────┐\n"
        chart += "│ Strategy Select │\n"
        chart += "└─────────┬───────┘\n"
        chart += "          │\n"
        chart += "┌─────────▼───────┐\n"
        chart += "│ Reasoning Chain │\n"
        chart += "└─────────┬───────┘\n"
        chart += "          │\n"
        chart += "┌─────────▼───────┐\n"
        chart += "│ Synthesis       │\n"
        chart += "└─────────┬───────┘\n"
        chart += "          │\n"
        chart += "┌─────────▼───────┐\n"
        chart += "│ Verification    │\n"
        chart += "└─────────────────┘\n"
        
        return chart
    
    def _extract_components(self, text: str) -> List[str]:
        """Extract problem components from decomposition"""
        # Simplified extraction logic
        return ["component1", "component2", "component3"]
    
    def _extract_confidence(self, text: str) -> float:
        """Extract confidence score from verification text"""
        # Simplified extraction - in practice, use regex or NLP
        return 0.85