"""
Model-Level Evaluation system.

Evaluates LLM quality metrics:
- Reasoning quality
- Language understanding
- Hallucination detection
- Token efficiency
"""

import uuid
import time
import logging
import statistics
from typing import Dict, Any, List
from collections import defaultdict

logger = logging.getLogger(__name__)


class ModelQualityEvaluator:
    """
    Model-Level Evaluation system.
    
    Evaluates LLM quality metrics:
    - Reasoning quality
    - Language understanding
    - Hallucination detection
    - Token efficiency
    """
    
    def __init__(self):
        self.evaluations: List[Dict[str, Any]] = []
        self.model_metrics: Dict[str, Dict[str, Any]] = defaultdict(
            lambda: {'evaluations': 0, 'metrics': defaultdict(list)}
        )
    
    def evaluate_response(self,
                         model_name: str,
                         prompt: str,
                         response: str,
                         ground_truth: str = None,
                         metadata: Dict[str, Any] = None) -> Dict[str, Any]:
        """Evaluate model response quality."""
        metrics = {}
        
        if ground_truth:
            metrics['exact_match'] = 1.0 if response.strip() == ground_truth.strip() else 0.0
            metrics['token_overlap'] = self._calculate_token_overlap(response, ground_truth)
        
        metrics['hallucination_score'] = self._detect_hallucination(prompt, response)
        metrics['reasoning_quality'] = self._assess_reasoning(response)
        metrics['token_efficiency'] = len(prompt.split()) / max(len(response.split()), 1)
        metrics['completeness'] = self._assess_completeness(response)
        
        evaluation = {
            'id': str(uuid.uuid4()),
            'model': model_name,
            'prompt': prompt,
            'response': response,
            'metrics': metrics,
            'metadata': metadata or {},
            'timestamp': time.time()
        }
        
        self.evaluations.append(evaluation)
        
        for metric_name, value in metrics.items():
            self.model_metrics[model_name]['metrics'][metric_name].append(value)
        self.model_metrics[model_name]['evaluations'] += 1
        
        return evaluation
    
    def _calculate_token_overlap(self, response: str, ground_truth: str) -> float:
        """Calculate token overlap similarity."""
        resp_tokens = set(response.lower().split())
        truth_tokens = set(ground_truth.lower().split())
        
        if not truth_tokens:
            return 0.0
        
        intersection = len(resp_tokens & truth_tokens)
        return intersection / len(truth_tokens)
    
    def _detect_hallucination(self, _prompt: str, response: str) -> float:
        """Detect potential hallucinations."""
        hallucination_indicators = [
            'according to my knowledge', 'i believe', 'i think',
            'probably', 'might be', 'could be'
        ]
        
        response_lower = response.lower()
        indicator_count = sum(1 for ind in hallucination_indicators if ind in response_lower)
        return min(indicator_count * 0.2, 1.0)
    
    def _assess_reasoning(self, response: str) -> float:
        """Assess reasoning quality."""
        reasoning_indicators = [
            'because', 'therefore', 'thus', 'since', 'as a result',
            'consequently', 'due to', 'step', 'first', 'second'
        ]
        
        response_lower = response.lower()
        indicator_count = sum(1 for ind in reasoning_indicators if ind in response_lower)
        return min(indicator_count * 0.15, 1.0)
    
    def _assess_completeness(self, response: str) -> float:
        """Assess response completeness."""
        word_count = len(response.split())
        has_punctuation = any(p in response for p in '.!?')
        
        completeness = 0.0
        if word_count > 10:
            completeness += 0.5
        if has_punctuation:
            completeness += 0.5
        
        return completeness
    
    def get_model_summary(self, model_name: str) -> Dict[str, Any]:
        """Get summary metrics for a model."""
        if model_name not in self.model_metrics:
            return {'error': 'Model not found'}
        
        model_data = self.model_metrics[model_name]
        summary = {
            'model': model_name,
            'total_evaluations': model_data['evaluations'],
            'metrics': {}
        }
        
        for metric_name, values in model_data['metrics'].items():
            if values:
                summary['metrics'][metric_name] = {
                    'mean': statistics.mean(values),
                    'min': min(values),
                    'max': max(values),
                    'stdev': statistics.stdev(values) if len(values) > 1 else 0
                }
        
        return summary
    
    def evaluate_hallucination(self, text: str, is_hallucination: bool, 
                               confidence: float = 1.0) -> Dict[str, Any]:
        """Evaluate text for hallucination."""
        return {
            'text': text,
            'is_hallucination': is_hallucination,
            'confidence': confidence,
            'timestamp': time.time()
        }
    
    def evaluate_reasoning(self, query: str, reasoning: str, 
                          answer: str, correct: bool) -> Dict[str, Any]:
        """Evaluate reasoning quality."""
        return {
            'query': query,
            'reasoning': reasoning,
            'answer': answer,
            'correct': correct,
            'timestamp': time.time()
        }
    
    def evaluate_token_efficiency(self, response: str, token_count: int,
                                  quality_score: float) -> Dict[str, Any]:
        """Evaluate token efficiency."""
        return {
            'response': response,
            'token_count': token_count,
            'quality_score': quality_score,
            'efficiency': quality_score / token_count if token_count > 0 else 0,
            'timestamp': time.time()
        }
    
    def get_quality_metrics(self) -> Dict[str, Any]:
        """Get overall quality metrics across all models."""
        return {
            'total_models': len(self.model_metrics),
            'total_evaluations': sum(m['evaluations'] for m in self.model_metrics.values()),
            'models': {name: self.get_model_summary(name) for name in self.model_metrics}
        }


__all__ = ['ModelQualityEvaluator']
