#!/usr/bin/env python3
"""
HumanEval benchmark evaluation for Stack 2.9.
Can run with local model (transformers/vLLM) or later via API.
"""

import json
import subprocess
import sys
from pathlib import Path
import argparse

def check_dependencies():
    """Check if human_eval package is available."""
    try:
        import human_eval
        return True
    except ImportError:
        print("❌ human_eval package not found")
        print("   Install with: pip install humaneval")
        return False

def evaluate_with_transformers(model_name: str, gpu: bool = True):
    """Evaluate using HuggingFace transformers."""
    try:
        from transformers import AutoModelForCausalLM, AutoTokenizer
        import torch
    except ImportError:
        print("❌ transformers not installed")
        return None

    print(f"🤖 Loading model: {model_name}")
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        device_map="auto" if gpu else None,
        torch_dtype=torch.float16 if gpu else torch.float32
    )

    # Load HumanEval data
    try:
        from human_eval.data import write_problems, read_problems
        from human_eval.evaluation import evaluate
    except ImportError:
        print("❌ human_eval package missing")
        return None

    # Run evaluation
    print("🧪 Running HumanEval evaluation...")
    results = evaluate(
        model=model,
        tokenizer=tokenizer,
        problems=read_problems(),
        temperature=0.2,
        max_length=2000
    )

    # Save results
    output = {
        "model": model_name,
        "benchmark": "HumanEval",
        "pass@1": results["pass@1"],
        "pass@10": results.get("pass@10", 0),
        "pass@100": results.get("pass@100", 0),
        "num_problems": len(read_problems()),
        "evaluated_at": datetime.now().isoformat()
    }

    return output

def evaluate_with_vllm(api_url: str = "http://localhost:8000"):
    """Evaluate using running vLLM server."""
    import openai
    from human_eval.data import read_problems

    client = openai.OpenAI(
        base_url=api_url,
        api_key="dummy"
    )

    problems = read_problems()
    print(f"🧪 Evaluating {len(problems)} HumanEval problems via vLLM...")

    # Implement evaluation loop
    pass_at_k = {"pass@1": 0, "pass@10": 0, "pass@100": 0}
    num_problems = len(problems)

    # Simplified - in practice need proper sampling
    for problem_id, problem in problems.items():
        prompt = problem["prompt"]
        response = client.chat.completions.create(
            model="stack-2.9",
            messages=[{"role": "user", "content": prompt}],
            max_tokens=500,
            temperature=0.2
        )
        completion = response.choices[0].message.content
        # Check if completion contains solution and tests pass
        # (Need actual test execution)
        # For now, placeholder

    # This is a placeholder - full implementation requires test execution

    output = {
        "model": "stack-2.9 (via vLLM)",
        "benchmark": "HumanEval",
        "note": "Evaluation script structure - requires full implementation with test execution",
        "num_problems": num_problems
    }

    return output

def generate_estimate():
    """Generate baseline estimate based on Qwen2.5-Coder numbers."""
    # Qwen2.5-Coder-32B reported ~82% on HumanEval
    # Our fine-tune should be similar or slightly better/worse
    estimate = {
        "model": "Stack 2.9 (estimate)",
        "benchmark": "HumanEval",
        "pass@1": 0.82,  # 82%
        "pass@10": 0.89,
        "pass@100": 0.92,
        "note": "Estimate based on Qwen2.5-Coder-32B baseline. Actual numbers after training.",
        "source": "https://qwenlm.github.io/blog/qwen2.5-coder/"
    }
    return estimate

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--model", type=str, help="HuggingFace model name or path")
    parser.add_argument("--vllm-api", type=str, default="http://localhost:8000", help="vLLM API URL")
    parser.add_argument("--output", type=str, default="stack-2.9-eval/results/humaneval.json")
    parser.add_argument("--estimate-only", action="store_true", help="Generate estimate without running")
    args = parser.parse_args()

    output_path = Path(args.output)
    output_path.parent.mkdir(parents=True, exist_ok=True)

    print("🔬 HumanEval Benchmark Evaluation")

    if args.estimate_only:
        print("📊 Generating estimate based on Qwen2.5-Coder baseline...")
        result = generate_estimate()
    elif args.model:
        if not check_dependencies():
            sys.exit(1)
        result = evaluate_with_transformers(args.model)
    else:
        # Try vLLM
        print(f"🌐 Connecting to vLLM at {args.vllm_api}")
        result = evaluate_with_vllm(args.vllm_api)

    if result:
        with open(output_path, 'w') as f:
            json.dump(result, f, indent=2)
        print(f"\n✅ Results saved to {output_path}")
        print(f"   Pass@1 (estimated/actual): {result.get('pass@1', 'N/A')*100:.1f}%" if result.get('pass@1') else "Result saved")
    else:
        print("❌ Evaluation failed")

if __name__ == "__main__":
    import datetime
    main()