#!/usr/bin/env python

# --- SIMULATION-ONLY BENCHMARK SCRIPT ---
# This script (quantum_efficiency_demo.py) is for simulation-only benchmarking and demonstration purposes.
# It does NOT use real quantum hardware or APIs and is NOT suitable for publication or production benchmarking.
# For publication-quality, production-ready benchmarks, use quantum_financial_benchmark.py and see docs/benchmarking_showcase.md.
# ----------------------------------------

"""
Quantum Efficiency Benchmarking Demo

This script demonstrates a simple benchmarking approach for quantum algorithms,
focusing on measuring key performance metrics like execution time, memory usage,
circuit depth, and result accuracy across different configurations.

It serves as a quick demonstration of the comprehensive benchmarking approach
implemented in the full test suite.

Note on IBM Quantum Platform compatibility:
- This demo uses platform-agnostic simulation for demonstration purposes
- For actual hardware testing, use the full test suite with IBM Quantum V2 primitives
- See docs/quantum_efficiency_benchmarking.md for IBM Quantum 2024 platform considerations
"""

import os
import sys
import time
import json
import logging
from datetime import datetime
from typing import Dict, List, Any, Optional
import random

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

class SimpleBenchmark:
    """A simple benchmarking class to measure quantum algorithm performance."""
    
    def __init__(self, output_dir: str = './benchmark_results'):
        """Initialize the benchmark with an output directory."""
        self.output_dir = output_dir
        os.makedirs(output_dir, exist_ok=True)
        
        # Initialize metrics storage
        self.metrics = {
            'execution_time': [],
            'memory_usage': [],
            'circuit_depth': [],
            'accuracy': [],
            'num_qubits': [],
            'backend': [],
            'configuration': []
        }
    
    def run_quantum_simulation(self, num_qubits: int, depth: int, backend: str = 'simulator') -> Dict[str, Any]:
        """
        Run a simulated quantum algorithm and collect performance metrics.
        
        Args:
            num_qubits: Number of qubits
            depth: Circuit depth
            backend: Simulated backend type
            
        Returns:
            Dictionary of performance metrics
        """
        logger.info(f"Running quantum simulation with {num_qubits} qubits, depth {depth} on {backend}")
        
        # Start timing
        start_time = time.time()
        
        # Simulate quantum computation
        state_size = 2**num_qubits
        
        # Simplified simulation that doesn't require numpy
        # Just track execution time scaling with problem size
        
        # Time increases with state size and circuit depth
        time.sleep(0.01 * num_qubits * depth / 10)  # Simulate computation time
        
        # For basic simulator, add more time
        if backend == 'basic_simulator':
            time.sleep(0.02 * num_qubits * depth / 10)  # Basic is slower
        
        # End timing
        execution_time = time.time() - start_time
        
        # Calculate memory usage (simplified approximation)
        memory_usage = 16 * (2**num_qubits) / 1024  # Approx MB for state vector
        
        # Calculate accuracy (simplified model)
        # Higher qubit count and depth typically reduces accuracy in real systems
        if backend == 'optimized_simulator':
            # Optimized has better accuracy
            accuracy = 0.99 - (0.01 * num_qubits / 10) - (0.005 * depth / 100)
        else:
            # Basic has worse accuracy
            accuracy = 0.95 - (0.015 * num_qubits / 10) - (0.01 * depth / 100)
        
        # Add some randomness for realism
        accuracy = max(0.5, min(0.99, accuracy + random.uniform(-0.05, 0.05)))
        
        # Collect metrics
        metrics = {
            'execution_time': execution_time,
            'memory_usage': memory_usage,
            'circuit_depth': depth,
            'accuracy': accuracy,
            'num_qubits': num_qubits,
            'backend': backend,
            'configuration': f"{num_qubits}q-{depth}d-{backend}"
        }
        
        # Store metrics
        for key, value in metrics.items():
            if key in self.metrics:
                self.metrics[key].append(value)
        
        logger.info(f"Completed simulation in {execution_time:.4f}s with {memory_usage:.2f}MB memory usage")
        return metrics
    
    def run_benchmarks(self):
        """Run a series of benchmarks with different configurations."""
        # Test configuration
        qubit_sizes = [2, 4, 8, 12]
        depths = [10, 50, 100]
        backends = ['basic_simulator', 'optimized_simulator']
        
        for backend in backends:
            for num_qubits in qubit_sizes:
                for depth in depths:
                    # Skip very large simulations
                    if num_qubits >= 10 and depth >= 100:
                        continue
                    
                    try:
                        self.run_quantum_simulation(num_qubits, depth, backend)
                    except Exception as e:
                        logger.error(f"Error running benchmark with {num_qubits} qubits, depth {depth}: {e}")
        
        # Save results
        self.save_results()
        logger.info("Results saved. Please generate plots using a Python environment with matplotlib installed.")
    
    def save_results(self):
        """Save benchmark results to a file."""
        results = {
            'timestamp': datetime.now().isoformat(),
            'metrics': self.metrics,
            'summary': {
                'total_benchmarks': len(self.metrics['execution_time']),
                'max_qubits': max(self.metrics['num_qubits']),
                'max_depth': max(self.metrics['circuit_depth']),
                'backends_tested': list(set(self.metrics['backend'])),
                'avg_execution_time': sum(self.metrics['execution_time']) / max(1, len(self.metrics['execution_time'])),
                'avg_memory_usage': sum(self.metrics['memory_usage']) / max(1, len(self.metrics['memory_usage'])),
                'avg_accuracy': sum(self.metrics['accuracy']) / max(1, len(self.metrics['accuracy']))
            }
        }
        
        # Save as JSON
        results_path = os.path.join(self.output_dir, f'benchmark_results_{datetime.now().strftime("%Y%m%d_%H%M%S")}.json')
        with open(results_path, 'w') as f:
            # Convert objects like datetime to strings for JSON serialization
            json.dump(results, f, indent=2, default=str)
        
        # Generate markdown report
        report_path = os.path.join(self.output_dir, f'benchmark_report_{datetime.now().strftime("%Y%m%d_%H%M%S")}.md')
        with open(report_path, 'w') as f:
            f.write("# Quantum Algorithm Efficiency Benchmark Report\n\n")
            f.write(f"Generated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n")
            
            f.write("## Summary\n\n")
            f.write(f"- Total benchmarks: {results['summary']['total_benchmarks']}\n")
            f.write(f"- Maximum qubits: {results['summary']['max_qubits']}\n")
            f.write(f"- Maximum circuit depth: {results['summary']['max_depth']}\n")
            f.write(f"- Backends tested: {', '.join(results['summary']['backends_tested'])}\n")
            f.write(f"- Average execution time: {results['summary']['avg_execution_time']:.4f}s\n")
            f.write(f"- Average memory usage: {results['summary']['avg_memory_usage']:.2f}MB\n")
            f.write(f"- Average accuracy: {results['summary']['avg_accuracy']:.4f}\n\n")
            
            f.write("## Efficiency Analysis\n\n")
            
            # Calculate efficiency scores
            efficiency_scores = [
                acc / (time + 0.001)
                for acc, time in zip(self.metrics['accuracy'], self.metrics['execution_time'])
            ]
            
            # Simple sorting to find the top indices (no numpy needed)
            indices_by_score = sorted(range(len(efficiency_scores)), key=lambda i: efficiency_scores[i], reverse=True)
            top_indices = indices_by_score[:5]  # Top 5
            
            f.write("### Top 5 Most Efficient Configurations\n\n")
            f.write("| Configuration | Qubits | Depth | Backend | Execution Time (s) | Accuracy | Efficiency Score |\n")
            f.write("|--------------|--------|-------|---------|-------------------|----------|------------------|\n")
            
            for i, idx in enumerate(top_indices, 1):
                f.write(
                    f"| #{i} | {self.metrics['num_qubits'][idx]} | "
                    f"{self.metrics['circuit_depth'][idx]} | "
                    f"{self.metrics['backend'][idx]} | "
                    f"{self.metrics['execution_time'][idx]:.4f} | "
                    f"{self.metrics['accuracy'][idx]:.4f} | "
                    f"{efficiency_scores[idx]:.4f} |\n"
                )
            
            f.write("\n### Performance Recommendations\n\n")
            
            # Simple recommendations based on results
            avg_efficiency = sum(efficiency_scores) / max(1, len(efficiency_scores))
            if avg_efficiency < 0.1:
                f.write("- ⚠️ Overall efficiency is low, consider optimizing the algorithm implementation.\n")
            
            # Find the best backend
            backend_scores = {}
            for backend in set(self.metrics['backend']):
                backend_indices = [i for i, b in enumerate(self.metrics['backend']) if b == backend]
                backend_scores[backend] = sum(efficiency_scores[i] for i in backend_indices) / len(backend_indices)
            
            best_backend = max(backend_scores.items(), key=lambda x: x[1])[0]
            f.write(f"- 🔹 The '{best_backend}' backend shows the best overall efficiency.\n")
            
            # Memory recommendations
            max_memory_usage = max(self.metrics['memory_usage'])
            max_memory_idx = self.metrics['memory_usage'].index(max_memory_usage)
            f.write(f"- 💾 Highest memory usage ({max_memory_usage:.2f}MB) observed with {self.metrics['num_qubits'][max_memory_idx]} qubits at depth {self.metrics['circuit_depth'][max_memory_idx]}.\n")
            
            f.write("\n## Next Steps\n\n")
            f.write("1. Implement the recommended optimizations based on the benchmark results.\n")
            f.write("2. Consider testing with additional circuit structures to better understand specific algorithm components.\n")
            f.write("3. Explore hardware-specific optimizations for the best-performing backend.\n")
            f.write("4. Integrate regular benchmarking into the CI/CD pipeline to track performance over time.\n")
            
            # Include instructions for visualization
            f.write("\n## Visualization\n\n")
            f.write("To visualize these results, use the following Python script:\n\n")
            f.write("```python\n")
            f.write("import json\n")
            f.write("import matplotlib.pyplot as plt\n")
            f.write("import numpy as np\n\n")
            f.write(f"# Load the benchmark results\n")
            f.write(f"with open('{results_path}', 'r') as f:\n")
            f.write("    results = json.load(f)\n\n")
            f.write("# Create plots\n")
            f.write("fig, axs = plt.subplots(2, 2, figsize=(12, 10))\n")
            f.write("fig.suptitle('Quantum Algorithm Efficiency Metrics', fontsize=16)\n\n")
            f.write("# Execution time vs qubits for each backend\n")
            f.write("for backend in set(results['metrics']['backend']):\n")
            f.write("    indices = [i for i, b in enumerate(results['metrics']['backend']) if b == backend]\n")
            f.write("    qubits = [results['metrics']['num_qubits'][i] for i in indices]\n")
            f.write("    times = [results['metrics']['execution_time'][i] for i in indices]\n")
            f.write("    axs[0, 0].scatter(qubits, times, label=backend)\n")
            f.write("axs[0, 0].set_title('Execution Time vs Qubits')\n")
            f.write("axs[0, 0].set_xlabel('Number of Qubits')\n")
            f.write("axs[0, 0].set_ylabel('Execution Time (s)')\n")
            f.write("axs[0, 0].legend()\n")
            f.write("axs[0, 0].grid(True)\n\n")
            f.write("# Memory usage vs qubits\n")
            f.write("for backend in set(results['metrics']['backend']):\n")
            f.write("    indices = [i for i, b in enumerate(results['metrics']['backend']) if b == backend]\n")
            f.write("    qubits = [results['metrics']['num_qubits'][i] for i in indices]\n")
            f.write("    memory = [results['metrics']['memory_usage'][i] for i in indices]\n")
            f.write("    axs[0, 1].scatter(qubits, memory, label=backend)\n")
            f.write("axs[0, 1].set_title('Memory Usage vs Qubits')\n") 
            f.write("axs[0, 1].set_xlabel('Number of Qubits')\n")
            f.write("axs[0, 1].set_ylabel('Memory Usage (MB)')\n")
            f.write("axs[0, 1].legend()\n")
            f.write("axs[0, 1].grid(True)\n\n")
            f.write("plt.tight_layout()\n")
            f.write("plt.savefig('quantum_efficiency_benchmark.png')\n")
            f.write("plt.show()\n")
            f.write("```\n")
        
        logger.info(f"Benchmark results saved to {results_path}")
        logger.info(f"Detailed report generated at {report_path}")

def main():
    """Run benchmark demonstration."""
    logger.info("Starting quantum efficiency benchmark demo")
    
    benchmark = SimpleBenchmark()
    benchmark.run_benchmarks()
    
    logger.info("Benchmark demo completed successfully")

if __name__ == '__main__':
    main() 