"""
Phase D: Fix Impact Estimator
Estimates the impact of recommended fixes WITHOUT executing them.
Keeps the tool safe and credible.
"""

from typing import Dict, Any, Optional


class FixImpactEstimator:
    """
    Estimates the impact of recommended fixes on metrics.
    
    This is prediction-based, not execution-based.
    The library recommends, does not auto-run.
    """
    
    def __init__(self):
        """Initialize the estimator."""
        pass
    
    def _estimate_retrieval_impact(
        self,
        fix: Dict[str, Any],
        current_metrics: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Estimate impact of retrieval changes."""
        
        current_recall = current_metrics.get("context_recall", {}).get("context_recall", 0.0)
        current_relevance = current_metrics.get("relevance", {}).get("relevance", 0.0)
        
        change_type = fix.get("change", "")
        
        if "top_k" in change_type.lower():
            current_top_k = fix.get("current_value", 5)
            recommended_top_k = fix.get("recommended_value", 7)
            
            # Heuristic: Each 2 additional chunks improves recall by ~0.1
            # but may slightly reduce relevance due to noise
            delta_k = recommended_top_k - current_top_k
            estimated_recall_gain = min(delta_k * 0.05, 0.3)  # Cap at +0.3
            estimated_relevance_loss = max(delta_k * -0.02, -0.1)  # Cap at -0.1
            
            return {
                "context_recall": f"+{estimated_recall_gain:.2f}",
                "relevance": f"{estimated_relevance_loss:+.2f}",
                "faithfulness": "+0.00",  # Retrieval doesn't affect faithfulness directly
                "confidence": 0.75,
                "rationale": (
                    f"Increasing top_k by {delta_k} typically improves recall but may "
                    "introduce some noise, slightly reducing relevance."
                )
            }
        
        elif "overlap" in change_type.lower():
            # Increasing chunk overlap improves recall with minimal relevance impact
            return {
                "context_recall": "+0.12",
                "relevance": "+0.03",
                "faithfulness": "+0.00",
                "confidence": 0.65,
                "rationale": (
                    "Increasing chunk overlap captures more context boundaries, "
                    "improving recall with minimal noise."
                )
            }
        
        return {
            "context_recall": "+0.10",
            "relevance": "+0.00",
            "faithfulness": "+0.00",
            "confidence": 0.60,
            "rationale": "Generic retrieval improvement estimate."
        }
    
    def _estimate_generation_impact(
        self,
        fix: Dict[str, Any],
        current_metrics: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Estimate impact of generation changes."""
        
        current_faithfulness = current_metrics.get("faithfulness", {}).get("faithfulness", 0.0)
        current_relevance = current_metrics.get("relevance", {}).get("relevance", 0.0)
        
        change_type = fix.get("change", "")
        
        if "temperature" in change_type.lower():
            current_temp = fix.get("current_value", 0.7)
            recommended_temp = fix.get("recommended_value", 0.4)
            
            # Heuristic: Lower temperature improves faithfulness but may reduce creativity
            temp_delta = current_temp - recommended_temp
            estimated_faithfulness_gain = min(temp_delta * 0.4, 0.35)  # Cap at +0.35
            estimated_relevance_impact = max(temp_delta * -0.05, -0.08)  # Slight loss
            
            return {
                "faithfulness": f"+{estimated_faithfulness_gain:.2f}",
                "relevance": f"{estimated_relevance_impact:+.2f}",
                "context_recall": "+0.00",  # Generation doesn't affect recall
                "confidence": 0.80,
                "rationale": (
                    f"Lowering temperature by {temp_delta:.1f} reduces hallucination risk, "
                    "improving faithfulness with minimal relevance impact."
                )
            }
        
        elif "prompt" in change_type.lower() or "grounding" in change_type.lower():
            # Tightening grounding improves faithfulness
            faithfulness_gap = 1.0 - current_faithfulness
            estimated_gain = min(faithfulness_gap * 0.5, 0.3)
            
            return {
                "faithfulness": f"+{estimated_gain:.2f}",
                "relevance": "+0.05",
                "context_recall": "+0.00",
                "confidence": 0.70,
                "rationale": (
                    "Adding explicit grounding instructions reduces hallucinations "
                    "and may improve answer focus."
                )
            }
        
        return {
            "faithfulness": "+0.15",
            "relevance": "+0.00",
            "context_recall": "+0.00",
            "confidence": 0.60,
            "rationale": "Generic generation improvement estimate."
        }
    
    def _estimate_instruction_impact(
        self,
        fix: Dict[str, Any],
        current_metrics: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Estimate impact of instruction/prompt changes."""
        
        current_relevance = current_metrics.get("relevance", {}).get("relevance", 0.0)
        
        relevance_gap = 1.0 - current_relevance
        estimated_gain = min(relevance_gap * 0.4, 0.25)
        
        return {
            "relevance": f"+{estimated_gain:.2f}",
            "faithfulness": "+0.05",  # May slightly improve
            "context_recall": "+0.00",
            "confidence": 0.65,
            "rationale": (
                "Revising system prompt to be less restrictive typically improves "
                "answer relevance while maintaining faithfulness."
            )
        }
    
    def _estimate_context_packing_impact(
        self,
        fix: Dict[str, Any],
        current_metrics: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Estimate impact of context packing changes."""
        
        change_type = fix.get("change", "")
        
        if "reduce" in change_type.lower() and "top_k" in change_type.lower():
            # Reducing top_k improves signal-to-noise
            return {
                "relevance": "+0.18",
                "faithfulness": "+0.08",
                "context_recall": "-0.05",  # Slight loss
                "confidence": 0.70,
                "rationale": (
                    "Reducing top_k improves signal-to-noise ratio, boosting relevance "
                    "and faithfulness with minimal recall loss."
                )
            }
        
        return {
            "relevance": "+0.12",
            "faithfulness": "+0.05",
            "context_recall": "+0.08",
            "confidence": 0.60,
            "rationale": "Generic context packing improvement estimate."
        }
    
    def _estimate_query_design_impact(
        self,
        fix: Dict[str, Any]
    ) -> Dict[str, Any]:
        """Estimate impact of query design changes."""
        
        change_type = fix.get("change", "")
        
        if "constraint" in change_type.lower():
            return {
                "feasibility": "OverConstrained → Answerable",
                "expected_outcome": "Query becomes answerable",
                "confidence": 0.90,
                "rationale": (
                    "Removing conflicting constraints makes the query answerable "
                    "by any RAG system."
                )
            }
        elif "specificity" in change_type.lower():
            return {
                "relevance": "+0.25",
                "context_recall": "+0.20",
                "faithfulness": "+0.10",
                "confidence": 0.75,
                "rationale": (
                    "Adding specificity improves retrieval precision and answer quality."
                )
            }
        elif "rephrase" in change_type.lower() or "documents" in change_type.lower():
            return {
                "context_recall": "+0.35",
                "relevance": "+0.15",
                "faithfulness": "+0.00",
                "confidence": 0.80,
                "rationale": (
                    "Addressing out-of-scope issues by rephrasing or adding documents "
                    "significantly improves coverage."
                )
            }
        
        return {
            "note": "Query is already well-formed",
            "confidence": 0.80
        }
    
    def estimate(
        self,
        counterfactual_fix: Optional[Dict[str, Any]],
        current_metrics: Dict[str, Any]
    ) -> Dict[str, Any]:
        """
        Estimate the impact of a counterfactual fix.
        
        Args:
            counterfactual_fix: From Phase C
            current_metrics: Current Phase 3 metrics
            
        Returns:
            Dictionary with:
            - estimated_impact: Predicted metric changes
            - confidence: 0.0-1.0
            - rationale: Why these estimates
        """
        
        if not counterfactual_fix:
            return {
                "estimated_impact": None,
                "note": "No fix to estimate. System is performing adequately."
            }
        
        fix_type = counterfactual_fix.get("type", "Unknown")
        
        # Route to appropriate estimator
        if fix_type == "Retrieval":
            impact = self._estimate_retrieval_impact(counterfactual_fix, current_metrics)
        elif fix_type == "Generation":
            impact = self._estimate_generation_impact(counterfactual_fix, current_metrics)
        elif fix_type == "Instruction":
            impact = self._estimate_instruction_impact(counterfactual_fix, current_metrics)
        elif fix_type == "ContextPacking":
            impact = self._estimate_context_packing_impact(counterfactual_fix, current_metrics)
        elif fix_type == "QueryDesign":
            impact = self._estimate_query_design_impact(counterfactual_fix)
        else:
            return {
                "estimated_impact": None,
                "note": f"No impact estimator for type: {fix_type}"
            }
        
        return {
            "estimated_impact": impact,
            "fix_details": {
                "type": fix_type,
                "change": counterfactual_fix.get("change", ""),
                "current": counterfactual_fix.get("current_value", ""),
                "recommended": counterfactual_fix.get("recommended_value", "")
            }
        }
