"""
GENERIC String Match Evaluator

Compares predicted output against expected output (simple string comparison).
NO assumptions about what the output represents (IDs, text, JSON, etc.).

Let GEPA discover the correct output format through evolution and feedback!
"""

from typing import Dict, Any

try:
    from .base_evaluator import BaseEvaluator
except ImportError:
    # For standalone testing
    import sys
    from pathlib import Path
    sys.path.insert(0, str(Path(__file__).parent.parent.parent))
    from gepa_optimizer.evaluation.base_evaluator import BaseEvaluator


class ScrollElementEvaluator(BaseEvaluator):
    """
    GENERIC evaluator - just compares strings!
    
    NO assumptions about:
    - Output format (element IDs, text, JSON, etc.)
    - Output structure
    - What the task is
    
    GEPA will learn the correct format through feedback and evolution.
    """
    
    def __init__(self, metric_weights: Dict[str, float] = None):
        """
        Initialize evaluator.
        
        Args:
            metric_weights: Weights for evaluation metrics
                          Default: {"output_match": 1.0}
        """
        default_weights = {
            "output_match": 1.0  # Simple string comparison
        }
        
        weights = metric_weights or default_weights
        super().__init__(metric_weights=weights)
    
    def evaluate(self, predicted: str, expected: str) -> Dict[str, float]:
        """
        Binary evaluation with element ID extraction.
        
        Phase 1 Implementation:
        - Extracts element IDs using regex patterns (flexible format support)
        - Uses INTEGER comparison for robustness (prevents "4" vs "14" bugs)
        - Binary scoring: correct element = 1.0, wrong/missing = 0.0
        
        Scoring Strategy:
        1. Extract element ID from both predicted and expected outputs
        2. Compare using integer arithmetic (not string comparison)
        3. Return 1.0 if match, 0.0 otherwise (no partial credit)
        
        Args:
            predicted: LLM's output (may include verbose explanation)
            expected: Expected output (may include verbose explanation)
            
        Returns:
            Dictionary with evaluation metrics and extracted element IDs
        """
        import re
        
        if not predicted or not expected:
            return {
                "content_match": 0.0,
                "output_match": 0.0,
                "composite_score": 0.0,
                "predicted_output": str(predicted).strip() if predicted else "",
                "expected_output": str(expected).strip() if expected else "",
                "predicted_element": "None",
                "expected_element": "None",
                "evaluation_reason": "❌ Empty or missing input/output"
            }
        
        predicted_str = str(predicted).strip()
        expected_str = str(expected).strip()
        
        # 1. Extract element numbers using MULTIPLE strategies (flexible!)
        # Strategy A: "Element: X" or "Element X" (explicit format)
        element_pattern_a = r'element[:\s]+(\d+)'
        
        # Strategy B: "element X" or "Element X" anywhere in text
        element_pattern_b = r'\belement\s+(\d+)\b'
        
        # Strategy C: Just find ANY number if other strategies fail (last resort)
        number_pattern = r'\b(\d+)\b'
        
        # Try to extract from predicted
        pred_match = re.search(element_pattern_a, predicted_str, re.IGNORECASE)
        if not pred_match:
            pred_match = re.search(element_pattern_b, predicted_str, re.IGNORECASE)
        if not pred_match:
            # Last resort: find first number in the text
            pred_match = re.search(number_pattern, predicted_str)
        
        # Try to extract from expected
        exp_match = re.search(element_pattern_a, expected_str, re.IGNORECASE)
        if not exp_match:
            exp_match = re.search(element_pattern_b, expected_str, re.IGNORECASE)
        if not exp_match:
            exp_match = re.search(number_pattern, expected_str)
        
        # 2. Check if we found element numbers in both
        if not exp_match:
            # Expected doesn't have element pattern - fallback to exact match
            content_score = 1.0 if predicted_str.lower() == expected_str.lower() else 0.0
        elif not pred_match:
            # Predicted doesn't have element number - WRONG
            content_score = 0.0
        else:
            # Both have element pattern - compare using INTEGER comparison
            pred_element = pred_match.group(1)
            exp_element = exp_match.group(1)
            
            # 🔥 Phase 1: Use INTEGER comparison for robustness
            # This prevents bugs like "4" != "14" string comparison issues
            try:
                pred_num = int(pred_element)
                exp_num = int(exp_element)
                
                # Integer comparison (more robust than string)
                content_score = 1.0 if pred_num == exp_num else 0.0
                
                # Log comparison for debugging
                if pred_num != exp_num:
                    import logging
                    logger = logging.getLogger(__name__)
                    logger.debug(f"Element mismatch: predicted={pred_num}, expected={exp_num}")
                    
            except (ValueError, TypeError) as e:
                # Fallback to string comparison if conversion fails
                import logging
                logger = logging.getLogger(__name__)
                logger.warning(f"Could not convert elements to integers: {e}, using string comparison")
                content_score = 1.0 if pred_element == exp_element else 0.0
        
        # 3. Binary score and reason
        if content_score == 1.0:
            composite_score = 1.0
            reason = "✅ Correct! Element number matches"
        else:
            composite_score = 0.0
            if pred_match and exp_match:
                reason = "❌ Wrong element number (predicted different element)"
            else:
                reason = "❌ Missing or invalid element number"
        
        pred_element = pred_match.group(1) if pred_match else "None"
        exp_element = exp_match.group(1) if exp_match else "None"
        
        # Detailed logging for transparency
        import logging
        logger = logging.getLogger(__name__)
        logger.info(f"\n{'─'*70}")
        logger.info(f"📊 EVALUATION DETAILS")
        logger.info(f"{'─'*70}")
        logger.info(f"   Expected: '{expected_str}' (Element: {exp_element})")
        logger.info(f"   Predicted: '{predicted_str}' (Element: {pred_element})")
        logger.info(f"   {'─'*66}")
        logger.info(f"   🎯 SCORE: {composite_score:.2f} - {reason}")
        logger.info(f"{'─'*70}\n")
        
        return {
            "content_match": content_score,
            "output_match": composite_score,  # This is what GEPA uses
            "composite_score": composite_score,
            "predicted_output": predicted_str,
            "expected_output": expected_str,
            "predicted_element": pred_element,
            "expected_element": exp_element,
            "evaluation_reason": reason
        }
    
    def get_evaluation_summary(self, results: list) -> Dict[str, Any]:
        """
        Get summary statistics for a batch of evaluations.
        
        Args:
            results: List of evaluation result dictionaries
            
        Returns:
            Summary statistics
        """
        if not results:
            return {
                "total_samples": 0,
                "accuracy": 0.0,
                "correct_predictions": 0
            }
        
        total = len(results)
        correct = sum(1 for r in results if r.get("output_match", 0.0) == 1.0)
        accuracy = correct / total if total > 0 else 0.0
        
        return {
            "total_samples": total,
            "accuracy": accuracy,
            "correct_predictions": correct,
            "incorrect_predictions": total - correct
        }


# Example usage and testing
if __name__ == "__main__":
    print("🚀 Testing Scroll Element Evaluator...")
    
    evaluator = ScrollElementEvaluator()
    
    # Test cases
    test_cases = [
        ("4", "4", True),
        ("Element: 4", "4", True),
        ("Element 4", "4", True),
        ("The element to interact with is 4", "4", True),
        ("Element ID: 4", "4", True),
        ("Click on element 4 to scroll", "4", True),
        ("5", "4", False),
        ("Element: 5", "4", False),
        ("No element found", "4", False),
        ("", "4", False),
    ]
    
    print("\n📝 Running test cases:")
    print("-" * 80)
    
    results = []
    for predicted, expected, should_match in test_cases:
        result = evaluator.evaluate(predicted, expected)
        match = result["composite_score"] == 1.0
        
        status = "✅" if match == should_match else "❌"
        print(f"{status} Predicted: '{predicted}' | Expected: '{expected}' | Match: {match}")
        
        results.append(result)
    
    # Summary
    print("\n📊 Summary:")
    summary = evaluator.get_evaluation_summary(results)
    print(f"   Total: {summary['total_samples']}")
    print(f"   Correct: {summary['correct_predictions']}")
    print(f"   Accuracy: {summary['accuracy']:.1%}")