#!/usr/bin/env python3
"""
Cross-Language Translation Benchmark for CodeReality-1T Dataset

This benchmark evaluates cross-language code translation systems on deliberately noisy data.
Analyzes equivalent implementations across different programming languages.

Status: PLANNED - Framework scaffold for future implementation
"""

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

def load_dataset_sample(data_dir: str, sample_size: int = 500) -> List[Dict]:
    """
    Load sample of repositories with cross-language implementations.

    Args:
        data_dir: Path to CodeReality-1T unified dataset
        sample_size: Number of repositories to sample

    Returns:
        List of repository data with multi-language content
    """
    # TODO: Implement repository loading with cross-language focus
    # Target repositories with:
    # - Multiple programming languages
    # - Similar algorithms in different languages
    # - Bindings or wrapper implementations
    print(f"Loading {sample_size} multi-language repositories...")
    return []

def extract_language_pairs(repositories: List[Dict]) -> List[Dict]:
    """
    Extract equivalent code implementations across different languages.

    Args:
        repositories: List of repository data

    Returns:
        List of language pairs with equivalent functionality
    """
    # TODO: Implement language pair extraction
    # Look for:
    # - Similar function names across languages
    # - Algorithm implementations in multiple languages
    # - Test files that indicate equivalent functionality
    # - Documentation mentioning language equivalence

    language_pairs = []

    common_pairs = [
        ("python", "javascript"),
        ("java", "c++"),
        ("python", "java"),
        ("javascript", "typescript"),
        ("c", "c++"),
        ("python", "go"),
        ("java", "c#"),
        ("rust", "c++")
    ]

    for repo in repositories:
        # Extract code snippets that appear to implement similar functionality
        pass

    return language_pairs

def simple_translation_evaluator(source_code: str, target_code: str,
                                source_lang: str, target_lang: str) -> Dict[str, Any]:
    """
    Simple rule-based translation evaluation for demonstration purposes.

    This is a baseline implementation - real translation evaluation would use
    sophisticated semantic analysis, execution testing, or ML-based similarity.

    Args:
        source_code: Source language implementation
        target_code: Target language implementation
        source_lang: Source programming language
        target_lang: Target programming language

    Returns:
        Translation quality assessment
    """
    # TODO: Implement comprehensive translation evaluation
    # Methods:
    # - Structural similarity analysis
    # - API usage pattern matching
    # - Execution behavior comparison
    # - Performance characteristic analysis

    results = {
        "translation_quality": 0.0,
        "structural_similarity": 0.0,
        "semantic_equivalence": 0.0,
        "syntax_correctness": 0.0,
        "functionality_preserved": False,
        "common_patterns": [],
        "differences": []
    }

    # Simple pattern matching for demonstration
    # Count similar keywords, structure patterns
    source_tokens = re.findall(r'\w+', source_code.lower())
    target_tokens = re.findall(r'\w+', target_code.lower())

    # Language-agnostic concepts
    common_concepts = ["function", "class", "method", "variable", "loop", "condition"]
    source_concepts = [t for t in source_tokens if t in common_concepts]
    target_concepts = [t for t in target_tokens if t in common_concepts]

    if source_concepts and target_concepts:
        structural_sim = len(set(source_concepts) & set(target_concepts)) / len(set(source_concepts) | set(target_concepts))
        results["structural_similarity"] = structural_sim

    # Mock semantic equivalence (in real implementation, would use AST analysis)
    results["semantic_equivalence"] = random.uniform(0.3, 0.8)
    results["syntax_correctness"] = random.uniform(0.6, 0.95)
    results["translation_quality"] = (results["structural_similarity"] +
                                    results["semantic_equivalence"] +
                                    results["syntax_correctness"]) / 3

    results["functionality_preserved"] = results["translation_quality"] > 0.6

    return results

def evaluate_translation_pairs(language_pairs: List[Dict]) -> Dict[str, Any]:
    """
    Evaluate translation quality across language pairs.

    Args:
        language_pairs: List of cross-language implementation pairs

    Returns:
        Comprehensive translation evaluation metrics
    """
    # TODO: Implement comprehensive evaluation
    # Metrics:
    # - Translation accuracy by language pair
    # - Semantic preservation scores
    # - Syntax correctness rates
    # - Performance equivalence

    total_pairs = len(language_pairs)
    successful_translations = 0
    quality_scores = []
    language_pair_performance = defaultdict(list)

    for pair in language_pairs:
        source_code = pair.get("source_code", "")
        target_code = pair.get("target_code", "")
        source_lang = pair.get("source_language", "unknown")
        target_lang = pair.get("target_language", "unknown")

        result = simple_translation_evaluator(source_code, target_code,
                                            source_lang, target_lang)

        quality = result["translation_quality"]
        quality_scores.append(quality)

        if result["functionality_preserved"]:
            successful_translations += 1

        pair_key = f"{source_lang}->{target_lang}"
        language_pair_performance[pair_key].append(quality)

    # Calculate aggregate metrics
    avg_quality = sum(quality_scores) / len(quality_scores) if quality_scores else 0
    success_rate = successful_translations / total_pairs if total_pairs > 0 else 0

    # Language pair performance
    pair_stats = {}
    for pair_key, scores in language_pair_performance.items():
        pair_stats[pair_key] = {
            "count": len(scores),
            "avg_quality": sum(scores) / len(scores),
            "success_rate": sum(1 for s in scores if s > 0.6) / len(scores)
        }

    return {
        "total_pairs": total_pairs,
        "successful_translations": successful_translations,
        "success_rate": success_rate,
        "average_quality": avg_quality,
        "quality_distribution": {
            "excellent": sum(1 for q in quality_scores if q > 0.8),
            "good": sum(1 for q in quality_scores if 0.6 < q <= 0.8),
            "fair": sum(1 for q in quality_scores if 0.4 < q <= 0.6),
            "poor": sum(1 for q in quality_scores if q <= 0.4)
        },
        "language_pair_performance": pair_stats
    }

def run_benchmark(repositories: List[Dict]) -> Dict[str, Any]:
    """
    Run complete cross-language translation benchmark.

    Args:
        repositories: List of repository data

    Returns:
        Complete benchmark results
    """
    print("Extracting cross-language pairs...")
    language_pairs = extract_language_pairs(repositories)

    print("Evaluating translation quality...")
    metrics = evaluate_translation_pairs(language_pairs)

    print("Analyzing language coverage...")
    language_coverage = defaultdict(int)
    for pair in language_pairs:
        source_lang = pair.get("source_language", "unknown")
        target_lang = pair.get("target_language", "unknown")
        language_coverage[source_lang] += 1
        language_coverage[target_lang] += 1

    return {
        "benchmark_info": {
            "name": "Cross-Language Translation Benchmark",
            "dataset": "CodeReality-1T",
            "version": "1.0.0",
            "description": "Evaluates code translation across programming languages",
            "status": "PLANNED - Framework scaffold"
        },
        "dataset_stats": {
            "total_repositories": len(repositories),
            "total_language_pairs": len(language_pairs),
            "avg_pairs_per_repo": len(language_pairs) / len(repositories) if repositories else 0,
            "unique_languages": len(language_coverage)
        },
        "translation_metrics": metrics,
        "language_coverage": dict(language_coverage),
        "insights": [
            "This is a planned benchmark - implementation needed",
            "Cross-language translation requires semantic understanding",
            "CodeReality-1T provides diverse language combinations",
            "Noisy dataset challenges automated translation systems"
        ],
        "recommendations": [
            "Implement AST-based semantic analysis",
            "Use execution-based validation when possible",
            "Consider language-specific idiom preservation",
            "Validate with human expert review for complex cases"
        ]
    }

def print_benchmark_results(results: Dict[str, Any]):
    """Print formatted benchmark results."""
    print("\n" + "="*60)
    print("CROSS-LANGUAGE TRANSLATION BENCHMARK RESULTS")
    print("="*60)

    info = results["benchmark_info"]
    print(f"Benchmark: {info['name']}")
    print(f"Dataset: {info['dataset']}")
    print(f"Status: {info['status']}")
    print(f"Description: {info['description']}")

    print("\nDataset Statistics:")
    stats = results["dataset_stats"]
    print(f"  Total Repositories: {stats['total_repositories']}")
    print(f"  Language Pairs Found: {stats['total_language_pairs']}")
    print(f"  Avg Pairs/Repo: {stats['avg_pairs_per_repo']:.2f}")
    print(f"  Unique Languages: {stats['unique_languages']}")

    print("\nTranslation Metrics:")
    metrics = results["translation_metrics"]
    print(f"  Success Rate: {metrics['success_rate']:.3f}")
    print(f"  Average Quality: {metrics['average_quality']:.3f}")

    print("\nQuality Distribution:")
    dist = metrics["quality_distribution"]
    print(f"  Excellent (>0.8): {dist['excellent']}")
    print(f"  Good (0.6-0.8): {dist['good']}")
    print(f"  Fair (0.4-0.6): {dist['fair']}")
    print(f"  Poor (≤0.4): {dist['poor']}")

    print("\nLanguage Coverage:")
    for lang, count in results["language_coverage"].items():
        print(f"  {lang}: {count}")

    print("\nKey Insights:")
    for insight in results["insights"]:
        print(f"  • {insight}")

    print("\nRecommendations:")
    for rec in results["recommendations"]:
        print(f"  • {rec}")

def main():
    """Run cross-language translation benchmark on CodeReality-1T dataset."""
    # Configuration
    data_dir = "/mnt/z/CodeReality_Final/unified_dataset"
    sample_size = 100  # Reduced for planning phase

    print("CodeReality-1T Cross-Language Translation Benchmark")
    print("Status: PLANNED - Framework scaffold only")
    print(f"Data directory: {data_dir}")
    print(f"Sample size: {sample_size}")

    # Load dataset sample
    print("\nLoading dataset sample...")
    repositories = load_dataset_sample(data_dir, sample_size)

    if not repositories:
        print("No repositories loaded - using mock data for demonstration")
        # Create mock data for demonstration
        repositories = [{"name": f"multilang_repo_{i}", "languages": ["python", "javascript"]} for i in range(10)]

    # Run benchmark
    results = run_benchmark(repositories)

    # Print results
    print_benchmark_results(results)

    # Save results
    output_file = "cross_language_translation_results.json"
    with open(output_file, 'w') as f:
        json.dump(results, f, indent=2)

    print(f"\nResults saved to: {output_file}")
    print("Note: This is a framework scaffold - full implementation needed")

if __name__ == "__main__":
    main()