"""
APE Benchmark Runner (v1.0.0 Scaffold)

Performance benchmarking infrastructure.

Author: David Van Aelst
Status: Scaffold - implementation pending
"""

from typing import Callable, Dict, List, Any
from dataclasses import dataclass


@dataclass
class BenchmarkResult:
    """
    Result of a benchmark run.
    """
    name: str
    iterations: int
    total_time: float
    avg_time: float
    min_time: float
    max_time: float
    std_dev: float
    
    def __str__(self):
        return (
            f"{self.name}:\n"
            f"  Iterations: {self.iterations}\n"
            f"  Total time: {self.total_time:.6f}s\n"
            f"  Average: {self.avg_time:.6f}s\n"
            f"  Min/Max: {self.min_time:.6f}s / {self.max_time:.6f}s\n"
            f"  Std dev: {self.std_dev:.6f}s"
        )


class BenchmarkRunner:
    """
    Benchmark runner for APE programs.
    
    Features:
        - Multiple iterations for statistical significance
        - Warmup runs to stabilize JIT/caching
        - Comparison between implementations
        - Performance profiling integration
    
    Example:
        runner = BenchmarkRunner()
        result = runner.benchmark("fibonacci", lambda: fib(30), iterations=100)
        print(result)
    
    TODO:
        - Implement timing infrastructure
        - Implement statistical analysis
        - Implement comparison tools
        - Implement profiling integration
    
    Author: David Van Aelst
    Status: v1.0.0 scaffold - implementation pending
    """
    
    def __init__(self, warmup_iterations: int = 5):
        """
        Initialize benchmark runner.
        
        Args:
            warmup_iterations: Number of warmup runs before timing
        """
        self._warmup_iterations = warmup_iterations
        self._results: List[BenchmarkResult] = []
    
    def benchmark(
        self, 
        name: str, 
        func: Callable[[], Any], 
        iterations: int = 100
    ) -> BenchmarkResult:
        """
        Run benchmark on a function.
        
        Args:
            name: Benchmark name
            func: Function to benchmark
            iterations: Number of iterations to run
        
        Returns:
            BenchmarkResult with timing statistics
        
        TODO: Implement benchmarking
        """
        raise NotImplementedError("Benchmarking not yet implemented")
    
    def compare(
        self, 
        implementations: Dict[str, Callable[[], Any]], 
        iterations: int = 100
    ) -> Dict[str, BenchmarkResult]:
        """
        Compare multiple implementations.
        
        Args:
            implementations: Dict of name -> function to benchmark
            iterations: Number of iterations per implementation
        
        Returns:
            Dict of name -> BenchmarkResult
        
        TODO: Implement comparison benchmarking
        """
        raise NotImplementedError("Comparison benchmarking not yet implemented")
    
    def profile(self, name: str, func: Callable[[], Any]) -> Any:
        """
        Profile execution with detailed performance data.
        
        Args:
            name: Profile name
            func: Function to profile
        
        Returns:
            Profile data
        
        TODO: Implement profiling
        """
        raise NotImplementedError("Profiling not yet implemented")
    
    def generate_report(self) -> str:
        """
        Generate benchmark report.
        
        Returns:
            Formatted report of all benchmark results
        
        TODO: Implement report generation
        """
        raise NotImplementedError("Report generation not yet implemented")


__all__ = ['BenchmarkRunner', 'BenchmarkResult']
