#!/usr/bin/env python3
"""
Run OpenHands agent experiments with concurrent execution.
"""
import os
import sys
import json
import argparse
import subprocess
from typing import List, Dict, Any
from concurrent.futures import ProcessPoolExecutor, as_completed
from tqdm import tqdm

# Default dataset path
DATASET_PATH = "/eval/dataset.jsonl"
RESULTS_DIR = "/experiments/openhands/results"

# Default configuration
DEFAULT_API_BASE = "http://localhost:60101/v1"
DEFAULT_MODEL = "qwen-3.5-27b"


def load_dataset(path: str = DATASET_PATH) -> List[Dict[str, Any]]:
    """Load questions from dataset.jsonl."""
    questions = []
    with open(path, 'r') as f:
        for line in f:
            line = line.strip()
            if line:
                questions.append(json.loads(line))
    return questions


def run_single_task(args: tuple) -> Dict[str, Any]:
    """
    Run a single OpenHands agent task.

    This function calls run_agent.py as a subprocess to avoid
    issues with forking and OpenHands internal state.

    Args:
        args: Tuple of (question, try_id, llm_name, api_base, model, enable_thinking)

    Returns:
        Dict with result information
    """
    question, try_id, llm_name, api_base, model, enable_thinking = args

    # Build command
    cmd = [
        '/opt/py312/bin/python3', '/experiments/openhands/exp/run_agent.py',
        '--prog', question['prog'],
        '--src-site-id', str(question['src_site_id']),
        '--to-take', str(question['to_take']),
        '--format', question.get('format', 'unknown'),
        '--try-id', str(try_id),
        '--api-base', api_base,
        '--model', model,
        '--llm-name', llm_name,
    ]

    if enable_thinking:
        cmd.append('--enable-thinking')

    try:
        result = subprocess.run(
            cmd,
            capture_output=True,
            text=True,
            timeout=10800 * 3,  # 3 hours per task
        )

        return {
            'success': result.returncode == 0,
            'question': question,
            'try_id': try_id,
            'stdout': result.stdout,
            'stderr': result.stderr,
        }

    except subprocess.TimeoutExpired:
        return {
            'success': False,
            'question': question,
            'try_id': try_id,
            'error': 'Task timed out',
        }
    except Exception as e:
        return {
            'success': False,
            'question': question,
            'try_id': try_id,
            'error': str(e),
        }


def main():
    parser = argparse.ArgumentParser(description="Run OpenHands agent experiments")

    # Model configuration
    parser.add_argument("--api-base", type=str, default=DEFAULT_API_BASE,
                        help="OpenAI-compatible API base URL")
    parser.add_argument("--model", type=str, default=DEFAULT_MODEL,
                        help="Model name to use")
    parser.add_argument("--llm-name", type=str, default=None,
                        help="LLM name for result directory (defaults to model name)")
    parser.add_argument("--enable-thinking", action="store_true",
                        help="Enable thinking/reasoning mode")

    # Concurrency settings
    parser.add_argument("--concurrency", type=int, default=1,
                        help="Number of concurrent agents (default: 1, recommend low for OpenHands)")

    # Experiment settings
    parser.add_argument("--num-tries", type=int, default=1,
                        help="Number of tries per question")
    parser.add_argument("--dataset", type=str, default=DATASET_PATH,
                        help="Path to dataset.jsonl")
    parser.add_argument("--num-questions", type=int, default=None,
                        help="Number of questions to test (default: all)")
    parser.add_argument("--start-idx", type=int, default=0,
                        help="Start index in dataset")

    args = parser.parse_args()

    # Set LLM name
    llm_name = args.llm_name if args.llm_name else args.model

    # Load dataset
    print(f"Loading dataset from {args.dataset}...")
    questions = load_dataset(args.dataset)
    print(f"Loaded {len(questions)} questions")

    # Apply question selection
    if args.start_idx > 0:
        questions = questions[args.start_idx:]
        print(f"Starting from index {args.start_idx}")

    if args.num_questions:
        questions = questions[:args.num_questions]
        print(f"Testing {args.num_questions} questions")

    # Build task list
    tasks = []
    for question in questions:
        for try_id in range(args.num_tries):
            tasks.append((
                question,
                try_id,
                llm_name,
                args.api_base,
                args.model,
                args.enable_thinking,
            ))

    total_tasks = len(tasks)
    print(f"\nTotal tasks: {total_tasks}")
    print(f"Questions: {len(questions)}, Tries per question: {args.num_tries}")
    print(f"Concurrency: {args.concurrency}")
    print(f"Model: {args.model}")
    print(f"Thinking mode: {args.enable_thinking}")

    # Create results directory
    os.makedirs(RESULTS_DIR, exist_ok=True)

    # Run with progress bar
    completed = 0
    successes = 0

    with ProcessPoolExecutor(max_workers=args.concurrency) as executor:
        futures = {executor.submit(run_single_task, task): task for task in tasks}

        with tqdm(total=total_tasks, desc="Processing") as pbar:
            for future in as_completed(futures):
                try:
                    result = future.result()
                    if result.get('success'):
                        successes += 1
                except Exception as e:
                    print(f"\nTask failed with exception: {e}")

                completed += 1
                pbar.update(1)
                pbar.set_postfix({"success": successes, "remaining": total_tasks - completed})

    print(f"\n\nExperiment completed!")
    print(f"Total tasks: {total_tasks}")
    print(f"Successful: {successes}")
    print(f"Failed: {total_tasks - successes}")
    print(f"Success rate: {successes / total_tasks * 100:.1f}%")


if __name__ == "__main__":
    main()