#!/usr/bin/env python3
"""
License Detection Benchmark for CodeReality-1T Dataset
Evaluates automated license classification systems
"""

import json
import os
from typing import Dict, List, Tuple
from collections import defaultdict
import random

def load_dataset_sample(data_dir: str, sample_size: int = 1000) -> List[Dict]:
    """Load random sample of repositories from dataset."""
    print(f"🔍 Loading sample of {sample_size} repositories...")

    repositories = []

    # Get available files
    files = [f for f in os.listdir(data_dir) if f.endswith('.jsonl')]
    random.shuffle(files)

    for filename in files[:10]:  # Sample from first 10 files
        file_path = os.path.join(data_dir, filename)
        try:
            with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:
                for line in f:
                    if len(repositories) >= sample_size:
                        break
                    try:
                        repo_data = json.loads(line)
                        repositories.append(repo_data)
                    except json.JSONDecodeError:
                        continue
        except Exception as e:
            print(f"⚠️ Error reading {filename}: {e}")
            continue

        if len(repositories) >= sample_size:
            break

    print(f"✅ Loaded {len(repositories)} repositories")
    return repositories

def extract_license_features(repo: Dict) -> Dict:
    """Extract features that could indicate license presence."""
    features = {
        'has_license_file': False,
        'has_readme': False,
        'license_keywords_count': 0,
        'copyright_mentions': 0,
        'file_count': 0,
        'detected_license': repo.get('license', 'Unknown')
    }

    files = repo.get('files', [])
    features['file_count'] = len(files)

    license_keywords = ['license', 'mit', 'apache', 'gpl', 'bsd', 'copyright']

    for file_obj in files:
        if isinstance(file_obj, dict):
            file_path = file_obj.get('path', '').lower()
            content = file_obj.get('content', '').lower()

            # Check for license files
            if any(keyword in file_path for keyword in ['license', 'copying', 'legal']):
                features['has_license_file'] = True

            # Check for README
            if 'readme' in file_path:
                features['has_readme'] = True

            # Count license keywords
            for keyword in license_keywords:
                features['license_keywords_count'] += content.count(keyword)

            # Count copyright mentions
            features['copyright_mentions'] += content.count('copyright')

    return features

def simple_license_classifier(features: Dict) -> str:
    """Simple rule-based license classifier for demonstration."""

    # Rule-based classification
    if features['has_license_file']:
        if features['license_keywords_count'] > 10:
            return 'MIT'  # Most common
        elif features['copyright_mentions'] > 5:
            return 'Apache-2.0'
        else:
            return 'GPL-3.0'
    elif features['has_readme'] and features['license_keywords_count'] > 3:
        return 'MIT'
    elif features['file_count'] > 50 and features['copyright_mentions'] > 2:
        return 'Apache-2.0'
    else:
        return 'Unknown'

def evaluate_license_detection(repositories: List[Dict]) -> Dict:
    """Evaluate license detection performance."""
    print("🧮 Evaluating license detection...")

    results = {
        'total_repos': len(repositories),
        'predictions': [],
        'ground_truth': [],
        'accuracy': 0.0,
        'license_distribution': defaultdict(int),
        'prediction_distribution': defaultdict(int)
    }

    for repo in repositories:
        features = extract_license_features(repo)
        predicted_license = simple_license_classifier(features)
        actual_license = features['detected_license']

        results['predictions'].append(predicted_license)
        results['ground_truth'].append(actual_license)
        results['license_distribution'][actual_license] += 1
        results['prediction_distribution'][predicted_license] += 1

    # Calculate accuracy (note: actual dataset has mostly 'Unknown' licenses)
    correct = sum(1 for p, a in zip(results['predictions'], results['ground_truth']) if p == a)
    results['accuracy'] = correct / len(repositories) if repositories else 0

    return results

def print_benchmark_results(results: Dict):
    """Print formatted benchmark results."""
    print("=" * 60)
    print("📊 LICENSE DETECTION BENCHMARK RESULTS")
    print("=" * 60)

    print(f"Total repositories evaluated: {results['total_repos']}")
    print(f"Overall accuracy: {results['accuracy']:.3f}")

    print("\n📈 Ground Truth Distribution:")
    for license_type, count in sorted(results['license_distribution'].items(), key=lambda x: x[1], reverse=True)[:10]:
        percentage = (count / results['total_repos']) * 100
        print(f"  {license_type}: {count} ({percentage:.1f}%)")

    print("\n🎯 Prediction Distribution:")
    for license_type, count in sorted(results['prediction_distribution'].items(), key=lambda x: x[1], reverse=True):
        percentage = (count / results['total_repos']) * 100
        print(f"  {license_type}: {count} ({percentage:.1f}%)")

    print("\n💡 Insights:")
    print("- CodeReality-1T is deliberately noisy with 0% license detection")
    print("- This benchmark demonstrates the challenge of license classification")
    print("- Most repositories lack clear licensing information")
    print("- Perfect for testing robustness of license detection systems")

def main():
    """Run license detection benchmark."""
    print("🚀 CodeReality-1T License Detection Benchmark")
    print("=" * 60)

    # Configuration
    data_dir = "/mnt/z/CodeReality_Final/unified_dataset"
    sample_size = 500

    if not os.path.exists(data_dir):
        print(f"❌ Dataset directory not found: {data_dir}")
        print("Please update the data_dir path to point to your CodeReality-1T dataset")
        return

    # Load dataset sample
    repositories = load_dataset_sample(data_dir, sample_size)

    if not repositories:
        print("❌ No repositories loaded. Check dataset path.")
        return

    # Run evaluation
    results = evaluate_license_detection(repositories)

    # Print results
    print_benchmark_results(results)

    # Save results
    output_file = "license_detection_results.json"
    with open(output_file, 'w') as f:
        # Convert defaultdict to regular dict for JSON serialization
        results_json = {
            'total_repos': results['total_repos'],
            'accuracy': results['accuracy'],
            'license_distribution': dict(results['license_distribution']),
            'prediction_distribution': dict(results['prediction_distribution'])
        }
        json.dump(results_json, f, indent=2)

    print(f"\n💾 Results saved to: {output_file}")

if __name__ == "__main__":
    main()