"""
Baseline Runner - Runs experiments using the "Old Way" (Single Model).

This script evaluates traditional single-model approaches for comparison
with the cross-model verification system.
"""

import json
import logging
import time
from pathlib import Path
from typing import Any

# Configure logging
logging.basicConfig(
    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)


class BaselineRunner:
    """
    Runs baseline experiments using a single model without adversarial verification.

    This provides the comparison baseline for measuring improvement
    from the cross-model verification approach.
    """

    def __init__(self, model_name: str = "gpt-4o", output_dir: str = "experiments/results"):
        """
        Initialize the baseline runner.

        Args:
            model_name: Model to use for baseline
            output_dir: Directory to save results
        """
        self.model_name = model_name
        self.output_dir = Path(output_dir)
        self.output_dir.mkdir(parents=True, exist_ok=True)

        logger.info(f"Baseline runner initialized with {model_name}")

    def run_dataset(self, dataset_path: str) -> dict[str, Any]:
        """
        Run the baseline on a dataset.

        Args:
            dataset_path: Path to dataset file

        Returns:
            Results dictionary with metrics
        """
        logger.info(f"Running baseline on dataset: {dataset_path}")

        # Load dataset
        tasks = self._load_dataset(dataset_path)

        results = {
            "model": self.model_name,
            "dataset": dataset_path,
            "total_tasks": len(tasks),
            "successful": 0,
            "failed": 0,
            "total_time": 0,
            "task_results": [],
        }

        # Run each task
        for i, task in enumerate(tasks):
            logger.info(f"Processing task {i+1}/{len(tasks)}")

            start_time = time.time()
            task_result = self._run_single_task(task)
            elapsed = time.time() - start_time

            task_result["time"] = elapsed
            results["task_results"].append(task_result)
            results["total_time"] += elapsed

            if task_result["success"]:
                results["successful"] += 1
            else:
                results["failed"] += 1

        # Calculate metrics
        results["accuracy"] = results["successful"] / results["total_tasks"]
        results["avg_time"] = results["total_time"] / results["total_tasks"]

        # Save results
        self._save_results(results, "baseline")

        logger.info(f"Baseline complete: {results['accuracy']:.2%} accuracy")
        return results

    def _run_single_task(self, task: dict[str, Any]) -> dict[str, Any]:
        """
        Run a single task with the baseline model.

        Args:
            task: Task dictionary

        Returns:
            Result dictionary
        """
        # TODO: Implement actual model execution
        # For now, return a placeholder
        return {
            "task_id": task.get("id", "unknown"),
            "success": False,
            "solution": "# Placeholder solution",
            "error": "Not implemented",
        }

    def _load_dataset(self, dataset_path: str) -> list[dict[str, Any]]:
        """Load dataset from file."""
        path = Path(dataset_path)

        if not path.exists():
            logger.warning(f"Dataset not found: {dataset_path}, using mock data")
            return self._get_mock_dataset()

        with open(path) as f:
            data = json.load(f)

        return data.get("tasks", [])

    def _get_mock_dataset(self) -> list[dict[str, Any]]:
        """Return a mock dataset for testing."""
        return [
            {
                "id": "task_1",
                "description": "Write a function to reverse a string",
                "expected_output": "function that reverses strings",
            },
            {
                "id": "task_2",
                "description": "Implement binary search",
                "expected_output": "binary search implementation",
            },
        ]

    def _save_results(self, results: dict[str, Any], experiment_name: str) -> None:
        """Save results to file."""
        timestamp = time.strftime("%Y%m%d_%H%M%S")
        filename = self.output_dir / f"{experiment_name}_{timestamp}.json"

        with open(filename, "w") as f:
            json.dump(results, f, indent=2)

        logger.info(f"Results saved to {filename}")


if __name__ == "__main__":
    runner = BaselineRunner()
    results = runner.run_dataset("experiments/datasets/sample.json")
    print("\nBaseline Results:")
    print(f"Accuracy: {results['accuracy']:.2%}")
    print(f"Avg Time: {results['avg_time']:.2f}s")
