"""
Advanced GPU Profiling Tools
===========================

Comprehensive GPU profiling, benchmarking, and performance analysis tools
for PyTorch neural networks and kernel optimization.

This module provides:
1. Advanced GPU kernel profiling
2. Memory bandwidth analysis
3. Compute utilization measurement
4. Performance bottleneck detection
5. Optimization recommendation system
6. Educational profiling examples

Author: TorchBridge Team
"""

import time
from collections.abc import Callable
from dataclasses import asdict, dataclass
from typing import Any

import torch
import torch.nn as nn
import torch.profiler


@dataclass
class KernelProfile:
    """Detailed kernel execution profile."""
    name: str
    duration_ms: float
    gpu_utilization: float
    memory_bandwidth_utilization: float
    arithmetic_intensity: float
    occupancy: float
    registers_per_thread: int
    shared_memory_usage: int
    grid_size: tuple[int, int, int]
    block_size: tuple[int, int, int]


@dataclass
class PerformanceMetrics:
    """Comprehensive performance metrics."""
    total_time_ms: float
    gpu_time_ms: float
    cpu_time_ms: float
    memory_transfer_time_ms: float
    kernel_time_ms: float
    memory_usage_mb: float
    peak_memory_mb: float
    compute_efficiency: float
    memory_efficiency: float
    throughput_ops_per_sec: float


class GPUProfiler:
    """
    Advanced GPU profiler with detailed kernel and memory analysis.

    Provides comprehensive profiling capabilities for PyTorch operations
    with focus on GPU kernel performance and optimization opportunities.
    """

    def __init__(self,
                 device: torch.device | None = None,
                 warmup_runs: int = 5,
                 benchmark_runs: int = 100):
        self.device = device or torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.warmup_runs = warmup_runs
        self.benchmark_runs = benchmark_runs
        self.profiling_results = []

    def profile_function(self,
                        func: Callable,
                        *args,
                        **kwargs) -> PerformanceMetrics:
        """
        Profile a function with comprehensive GPU metrics.

        Args:
            func: Function to profile
            *args: Function arguments
            **kwargs: Function keyword arguments

        Returns:
            PerformanceMetrics with detailed performance data
        """
        # Move arguments to device
        args = tuple(arg.to(self.device) if isinstance(arg, torch.Tensor) else arg for arg in args)
        for key, value in kwargs.items():
            if isinstance(value, torch.Tensor):
                kwargs[key] = value.to(self.device)

        # Warmup
        for _ in range(self.warmup_runs):
            with torch.no_grad():
                func(*args, **kwargs)

        torch.cuda.synchronize()
        torch.cuda.empty_cache()

        # Memory measurements
        initial_memory = torch.cuda.memory_allocated() / 1024**2  # MB
        torch.cuda.reset_peak_memory_stats()

        # Timing measurements
        start_time = time.perf_counter()
        torch.cuda.synchronize()

        gpu_start = torch.cuda.Event(enable_timing=True)
        gpu_end = torch.cuda.Event(enable_timing=True)

        gpu_start.record()

        # Execute function
        results = []
        for _ in range(self.benchmark_runs):
            result = func(*args, **kwargs)
            results.append(result)

        gpu_end.record()
        torch.cuda.synchronize()

        end_time = time.perf_counter()

        # Calculate metrics
        total_time_ms = (end_time - start_time) * 1000
        gpu_time_ms = gpu_start.elapsed_time(gpu_end)
        current_memory = torch.cuda.memory_allocated() / 1024**2
        peak_memory = torch.cuda.max_memory_allocated() / 1024**2

        # Estimate memory bandwidth utilization
        memory_accessed = peak_memory - initial_memory
        theoretical_bandwidth = self._get_theoretical_memory_bandwidth()
        memory_bandwidth_utilization = (memory_accessed * self.benchmark_runs / (gpu_time_ms / 1000)) / theoretical_bandwidth

        # Estimate compute efficiency
        ops_per_run = self._estimate_operations(func, args, kwargs)
        total_ops = ops_per_run * self.benchmark_runs
        compute_efficiency = self._calculate_compute_efficiency(total_ops, gpu_time_ms)

        return PerformanceMetrics(
            total_time_ms=total_time_ms,
            gpu_time_ms=gpu_time_ms,
            cpu_time_ms=total_time_ms - gpu_time_ms,
            memory_transfer_time_ms=0.0,  # NOTE: Inputs pre-transferred to GPU before timing
            kernel_time_ms=gpu_time_ms,
            memory_usage_mb=current_memory,
            peak_memory_mb=peak_memory,
            compute_efficiency=compute_efficiency,
            memory_efficiency=min(memory_bandwidth_utilization, 1.0),
            throughput_ops_per_sec=total_ops / (gpu_time_ms / 1000) if gpu_time_ms > 0 else 0.0
        )

    def profile_model(self,
                     model: nn.Module,
                     sample_inputs: torch.Tensor | tuple[torch.Tensor, ...],
                     include_backward: bool = True,
                     detailed_kernels: bool = False) -> dict[str, Any]:
        """
        Profile a complete model with detailed layer-by-layer analysis.

        Args:
            model: PyTorch model to profile
            sample_inputs: Sample input tensors
            include_backward: Include backward pass profiling
            detailed_kernels: Include detailed kernel profiling

        Returns:
            Comprehensive profiling results
        """
        model = model.to(self.device)
        if isinstance(sample_inputs, torch.Tensor):
            sample_inputs = sample_inputs.to(self.device)
        else:
            sample_inputs = tuple(inp.to(self.device) for inp in sample_inputs)

        profiling_results = {
            'model_info': self._get_model_info(model),
            'forward_pass': {},
            'backward_pass': {},
            'layer_profiles': {},
            'memory_analysis': {},
            'optimization_recommendations': []
        }

        # Profile forward pass
        if isinstance(sample_inputs, tuple):
            forward_metrics = self.profile_function(model, *sample_inputs)
        else:
            forward_metrics = self.profile_function(model, sample_inputs)

        profiling_results['forward_pass'] = asdict(forward_metrics)

        # Profile backward pass
        if include_backward:
            def backward_pass():
                if isinstance(sample_inputs, tuple):
                    outputs = model(*sample_inputs)
                else:
                    outputs = model(sample_inputs)

                if isinstance(outputs, torch.Tensor):
                    loss = outputs.sum()
                else:
                    loss = sum(out.sum() for out in outputs if isinstance(out, torch.Tensor))

                loss.backward()
                model.zero_grad()

            backward_metrics = self.profile_function(backward_pass)
            profiling_results['backward_pass'] = asdict(backward_metrics)

        # Detailed layer profiling
        if detailed_kernels:
            profiling_results['layer_profiles'] = self._profile_layers(model, sample_inputs)

        # Memory analysis
        profiling_results['memory_analysis'] = self._analyze_memory_patterns(model, sample_inputs)

        # Generate optimization recommendations
        profiling_results['optimization_recommendations'] = self._generate_recommendations(
            forward_metrics,
            backward_metrics if include_backward else None
        )

        return profiling_results

    def benchmark_implementations(self,
                                implementations: dict[str, Callable],
                                *args,
                                **kwargs) -> dict[str, PerformanceMetrics]:
        """
        Benchmark multiple implementations of the same operation.

        Args:
            implementations: Dictionary mapping names to implementation functions
            *args: Arguments to pass to implementations
            **kwargs: Keyword arguments to pass to implementations

        Returns:
            Dictionary mapping implementation names to performance metrics
        """
        results = {}

        for name, impl in implementations.items():
            print(f"Benchmarking {name}...")
            try:
                metrics = self.profile_function(impl, *args, **kwargs)
                results[name] = metrics
            except Exception as e:
                print(f"Error benchmarking {name}: {e}")
                continue

        return results

    def _get_model_info(self, model: nn.Module) -> dict[str, Any]:
        """Get basic model information."""
        total_params = sum(p.numel() for p in model.parameters())
        trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)

        return {
            'total_parameters': total_params,
            'trainable_parameters': trainable_params,
            'model_size_mb': total_params * 4 / 1024**2,  # Assuming float32
            'num_layers': len(list(model.modules())) - 1  # Exclude root module
        }

    def _profile_layers(self, model: nn.Module, sample_inputs) -> dict[str, dict[str, Any]]:
        """Profile individual layers of the model."""
        layer_profiles = {}

        # Register hooks for layer profiling
        layer_times = {}

        def make_hook(name):
            def hook(module, input, output):
                torch.cuda.synchronize()
                if name in layer_times:
                    layer_times[name].append(time.time())
                else:
                    layer_times[name] = [time.time()]
            return hook

        hooks = []
        for name, module in model.named_modules():
            if len(list(module.children())) == 0:  # Leaf modules only
                hook = module.register_forward_hook(make_hook(name))
                hooks.append(hook)

        # Run model with timing
        model.eval()
        with torch.no_grad():
            if isinstance(sample_inputs, tuple):
                model(*sample_inputs)
            else:
                model(sample_inputs)

        # Remove hooks
        for hook in hooks:
            hook.remove()

        # Process timing data
        for name, times in layer_times.items():
            if len(times) >= 2:
                duration = times[-1] - times[0]
                layer_profiles[name] = {
                    'duration_ms': duration * 1000,
                    'relative_time': 0.0  # Will be calculated after all layers
                }

        # Calculate relative times
        total_time = sum(prof['duration_ms'] for prof in layer_profiles.values())
        if total_time > 0:
            for prof in layer_profiles.values():
                prof['relative_time'] = prof['duration_ms'] / total_time

        return layer_profiles

    def _analyze_memory_patterns(self, model: nn.Module, sample_inputs) -> dict[str, Any]:
        """Analyze memory usage patterns."""
        torch.cuda.empty_cache()
        torch.cuda.reset_peak_memory_stats()

        initial_memory = torch.cuda.memory_allocated()

        # Forward pass memory analysis
        if isinstance(sample_inputs, tuple):
            outputs = model(*sample_inputs)
        else:
            outputs = model(sample_inputs)

        forward_peak = torch.cuda.max_memory_allocated()

        # Backward pass memory analysis
        if isinstance(outputs, torch.Tensor):
            loss = outputs.sum()
        else:
            loss = sum(
                (out.sum() for out in outputs if isinstance(out, torch.Tensor)),
                torch.tensor(0.0),
            )

        loss.backward()
        backward_peak = torch.cuda.max_memory_allocated()

        return {
            'initial_memory_mb': initial_memory / 1024**2,
            'forward_peak_mb': forward_peak / 1024**2,
            'backward_peak_mb': backward_peak / 1024**2,
            'total_memory_increase_mb': (backward_peak - initial_memory) / 1024**2,
            'memory_efficiency': initial_memory / forward_peak if forward_peak > 0 else 0.0
        }

    def _generate_recommendations(self,
                                forward_metrics: PerformanceMetrics,
                                backward_metrics: PerformanceMetrics | None = None) -> list[str]:
        """Generate optimization recommendations based on profiling results."""
        recommendations = []

        # Memory efficiency recommendations
        if forward_metrics.memory_efficiency < 0.5:
            recommendations.append("Consider using gradient checkpointing to reduce memory usage")

        if forward_metrics.peak_memory_mb > 8000:  # 8GB threshold
            recommendations.append("Large memory usage detected - consider reducing batch size or using memory-efficient attention")

        # Compute efficiency recommendations
        if forward_metrics.compute_efficiency < 0.3:
            recommendations.append("Low compute efficiency - consider kernel fusion or using torch.compile")

        if forward_metrics.memory_efficiency > 0.8 and forward_metrics.compute_efficiency < 0.5:
            recommendations.append("Memory-bound operation detected - focus on reducing memory access overhead")

        # Performance recommendations
        if backward_metrics and backward_metrics.total_time_ms > forward_metrics.total_time_ms * 3:
            recommendations.append("Backward pass is significantly slower - consider mixed precision training")

        if forward_metrics.throughput_ops_per_sec < 1e6:  # Arbitrary threshold
            recommendations.append("Low throughput detected - consider batch size optimization or model parallelism")

        return recommendations

    def _get_theoretical_memory_bandwidth(self) -> float:
        """Get theoretical memory bandwidth for the current GPU (GB/s)."""
        # This is a simplified estimation - real implementation would query GPU properties
        gpu_name = torch.cuda.get_device_name(self.device)

        # Rough estimates for common GPUs (in GB/s)
        bandwidth_estimates = {
            'V100': 900,
            'A100': 1555,
            'H100': 3000,
            'RTX 3080': 760,
            'RTX 3090': 936,
            'RTX 4090': 1008
        }

        for gpu_type, bandwidth in bandwidth_estimates.items():
            if gpu_type in gpu_name:
                return bandwidth

        return 500  # Default estimate

    def _estimate_operations(self, func: Callable, args: tuple, kwargs: dict) -> int:
        """Estimate the number of operations in a function call."""
        # This is a simplified estimation
        # Real implementation would analyze the computational graph
        if hasattr(func, '__self__') and isinstance(func.__self__, nn.Module):
            return self._estimate_module_operations(func.__self__, args)
        return 1000000  # Default estimate

    def _estimate_module_operations(self, module: nn.Module, inputs: tuple) -> int:
        """Estimate operations for a PyTorch module."""
        total_ops = 0

        for _name, layer in module.named_modules():
            if isinstance(layer, nn.Linear):
                input_size = layer.in_features
                output_size = layer.out_features
                batch_size = inputs[0].shape[0] if inputs and len(inputs[0].shape) > 0 else 1
                total_ops += batch_size * input_size * output_size * 2  # FMA operations
            elif isinstance(layer, nn.Conv2d):
                # Simplified convolution operation estimation
                if inputs and len(inputs[0].shape) >= 4:
                    batch, channels, height, width = inputs[0].shape[:4]
                    kernel_ops = layer.kernel_size[0] * layer.kernel_size[1] * layer.in_channels
                    output_elements = batch * layer.out_channels * height * width
                    total_ops += kernel_ops * output_elements

        return max(total_ops, 1000000)  # Minimum estimate

    def _calculate_compute_efficiency(self, operations: int, gpu_time_ms: float) -> float:
        """Calculate compute efficiency based on operations and time."""
        if gpu_time_ms <= 0:
            return 0.0

        ops_per_second = operations / (gpu_time_ms / 1000)

        # Get theoretical peak FLOPS for current GPU (simplified)
        gpu_name = torch.cuda.get_device_name(self.device)

        # Rough estimates for peak FLOPS (in GFLOPS)
        peak_flops_estimates = {
            'V100': 15700,
            'A100': 19500,
            'H100': 67000,
            'RTX 3080': 29700,
            'RTX 3090': 35600,
            'RTX 4090': 83000
        }

        theoretical_peak = 10000 * 1e9  # Default 10 TFLOPS
        for gpu_type, flops in peak_flops_estimates.items():
            if gpu_type in gpu_name:
                theoretical_peak = flops * 1e9
                break

        return min(ops_per_second / theoretical_peak, 1.0)


class PerformanceBenchmark:
    """
    Comprehensive benchmarking suite for PyTorch operations.

    Provides standardized benchmarks for common operations and optimization techniques.
    """

    def __init__(self, device: torch.device | None = None):
        self.device = device or torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        self.profiler = GPUProfiler(device)

    def benchmark_attention_implementations(self,
                                          batch_size: int = 8,
                                          seq_len: int = 512,
                                          embed_dim: int = 512,
                                          num_heads: int = 8) -> dict[str, PerformanceMetrics]:
        """Benchmark different attention implementations."""

        # Create test data
        q = torch.randn(batch_size, seq_len, embed_dim, device=self.device)
        k = torch.randn(batch_size, seq_len, embed_dim, device=self.device)
        v = torch.randn(batch_size, seq_len, embed_dim, device=self.device)

        implementations = {}

        # Standard attention
        def standard_attention(q, k, v):
            scale = (embed_dim // num_heads) ** -0.5
            attn = torch.matmul(q, k.transpose(-2, -1)) * scale
            attn = torch.softmax(attn, dim=-1)
            return torch.matmul(attn, v)

        implementations['standard'] = standard_attention

        # Flash attention if available
        if hasattr(torch.nn.functional, 'scaled_dot_product_attention'):
            def flash_attention(q, k, v):
                return torch.nn.functional.scaled_dot_product_attention(q, k, v)
            implementations['flash'] = flash_attention

        # Chunked attention
        def chunked_attention(q, k, v, chunk_size=128):
            batch_size, seq_len, embed_dim = q.shape
            outputs = []

            for i in range(0, seq_len, chunk_size):
                q_chunk = q[:, i:i+chunk_size]
                attn_chunk = torch.matmul(q_chunk, k.transpose(-2, -1))
                attn_chunk = torch.softmax(attn_chunk, dim=-1)
                out_chunk = torch.matmul(attn_chunk, v)
                outputs.append(out_chunk)

            return torch.cat(outputs, dim=1)

        implementations['chunked'] = chunked_attention

        return self.profiler.benchmark_implementations(implementations, q, k, v)

    def benchmark_normalization_implementations(self,
                                              batch_size: int = 32,
                                              seq_len: int = 512,
                                              embed_dim: int = 512) -> dict[str, PerformanceMetrics]:
        """Benchmark different normalization implementations."""

        x = torch.randn(batch_size, seq_len, embed_dim, device=self.device)

        implementations = {}

        # Layer normalization
        def layer_norm(x):
            return torch.layer_norm(x, (embed_dim,))

        implementations['layer_norm'] = layer_norm

        # RMS normalization
        def rms_norm(x):
            variance = x.pow(2).mean(-1, keepdim=True)
            return x * torch.rsqrt(variance + 1e-5)

        implementations['rms_norm'] = rms_norm

        # Manual layer norm
        def manual_layer_norm(x):
            mean = x.mean(-1, keepdim=True)
            var = x.var(-1, keepdim=True, unbiased=False)
            return (x - mean) / torch.sqrt(var + 1e-5)

        implementations['manual_layer_norm'] = manual_layer_norm

        return self.profiler.benchmark_implementations(implementations, x)

    def benchmark_activation_functions(self,
                                     batch_size: int = 32,
                                     seq_len: int = 512,
                                     embed_dim: int = 512) -> dict[str, PerformanceMetrics]:
        """Benchmark different activation function implementations."""

        x = torch.randn(batch_size, seq_len, embed_dim, device=self.device)

        implementations = {
            'relu': torch.relu,
            'gelu': torch.nn.functional.gelu,
            'gelu_tanh': lambda x: torch.nn.functional.gelu(x, approximate='tanh'),
            'swish': lambda x: x * torch.sigmoid(x),
            'mish': lambda x: x * torch.tanh(torch.nn.functional.softplus(x))
        }

        return self.profiler.benchmark_implementations(implementations, x)


def demonstrate_profiling_tools():
    """
    Comprehensive demonstration of GPU profiling tools.
    """
    print(" Advanced GPU Profiling Tools Demonstration")
    print("=" * 60)

    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print(f"Using device: {device}")

    if device.type == 'cuda':
        print(f"GPU: {torch.cuda.get_device_name()}")
        print(f"Memory: {torch.cuda.get_device_properties(device).total_memory / 1024**3:.1f} GB")

    # Initialize profiler
    profiler = GPUProfiler(device)
    benchmark = PerformanceBenchmark(device)

    # Create sample model for profiling
    class ProfilingTestModel(nn.Module):
        def __init__(self, embed_dim=512, num_heads=8):
            super().__init__()
            self.attention = nn.MultiheadAttention(embed_dim, num_heads, batch_first=True)
            self.norm1 = nn.LayerNorm(embed_dim)
            self.ffn = nn.Sequential(
                nn.Linear(embed_dim, embed_dim * 4),
                nn.GELU(),
                nn.Linear(embed_dim * 4, embed_dim)
            )
            self.norm2 = nn.LayerNorm(embed_dim)

        def forward(self, x):
            # Self-attention block
            attn_out, _ = self.attention(x, x, x)
            x = self.norm1(x + attn_out)

            # Feed-forward block
            ffn_out = self.ffn(x)
            x = self.norm2(x + ffn_out)

            return x

    model = ProfilingTestModel().to(device)
    sample_input = torch.randn(8, 128, 512, device=device)

    print("\n Model Architecture:")
    print("  Embed dimension: 512")
    print("  Number of heads: 8")
    print(f"  Parameters: {sum(p.numel() for p in model.parameters()):,}")

    # Model profiling
    print("\n Comprehensive Model Profiling:")
    profile_results = profiler.profile_model(model, sample_input, include_backward=True, detailed_kernels=True)

    # Display results
    forward_metrics = profile_results['forward_pass']
    print("  Forward pass:")
    print(f"    Time: {forward_metrics['gpu_time_ms']:.2f} ms")
    print(f"    Memory: {forward_metrics['peak_memory_mb']:.1f} MB peak")
    print(f"    Compute efficiency: {forward_metrics['compute_efficiency']:.1%}")
    print(f"    Memory efficiency: {forward_metrics['memory_efficiency']:.1%}")

    if 'backward_pass' in profile_results:
        backward_metrics = profile_results['backward_pass']
        print("  Backward pass:")
        print(f"    Time: {backward_metrics['gpu_time_ms']:.2f} ms")
        print(f"    Memory: {backward_metrics['peak_memory_mb']:.1f} MB peak")

    # Display layer profiling
    if profile_results['layer_profiles']:
        print("\n Layer-by-Layer Profiling:")
        sorted_layers = sorted(
            profile_results['layer_profiles'].items(),
            key=lambda x: x[1]['duration_ms'],
            reverse=True
        )

        for layer_name, layer_prof in sorted_layers[:5]:  # Top 5 layers
            print(f"    {layer_name}: {layer_prof['duration_ms']:.2f} ms "
                  f"({layer_prof['relative_time']:.1%})")

    # Optimization recommendations
    print("\n Optimization Recommendations:")
    for rec in profile_results['optimization_recommendations']:
        print(f"    • {rec}")

    # Attention benchmarks
    print("\n Attention Implementation Benchmark:")
    attention_results = benchmark.benchmark_attention_implementations()

    for impl_name, metrics in attention_results.items():
        print(f"    {impl_name.capitalize():>12}: {metrics.gpu_time_ms:.2f} ms "
              f"({metrics.compute_efficiency:.1%} compute, {metrics.memory_efficiency:.1%} memory)")

    # Find best attention implementation
    best_attention = min(attention_results.items(), key=lambda x: x[1].gpu_time_ms)
    baseline = attention_results.get('standard')
    if baseline and best_attention[0] != 'standard':
        speedup = baseline.gpu_time_ms / best_attention[1].gpu_time_ms
        print(f"    Best: {best_attention[0]} ({speedup:.2f}x speedup)")

    # Normalization benchmarks
    print("\n Normalization Implementation Benchmark:")
    norm_results = benchmark.benchmark_normalization_implementations()

    for impl_name, metrics in norm_results.items():
        print(f"    {impl_name.replace('_', ' ').title():>15}: {metrics.gpu_time_ms:.2f} ms")

    # Activation function benchmarks
    print("\n Activation Function Benchmark:")
    activation_results = benchmark.benchmark_activation_functions()

    for impl_name, metrics in activation_results.items():
        print(f"    {impl_name.upper():>8}: {metrics.gpu_time_ms:.2f} ms")

    # Memory analysis
    print("\n Memory Analysis:")
    memory_analysis = profile_results['memory_analysis']
    print(f"    Forward peak: {memory_analysis['forward_peak_mb']:.1f} MB")
    print(f"    Backward peak: {memory_analysis['backward_peak_mb']:.1f} MB")
    print(f"    Total increase: {memory_analysis['total_memory_increase_mb']:.1f} MB")
    print(f"    Memory efficiency: {memory_analysis['memory_efficiency']:.1%}")

    # Performance comparison with torch.compile
    print("\n torch.compile Performance Comparison:")

    # Original model
    original_metrics = profiler.profile_function(model, sample_input)

    # Compiled model
    compiled_model = torch.compile(model)
    compiled_metrics = profiler.profile_function(compiled_model, sample_input)

    speedup = original_metrics.gpu_time_ms / compiled_metrics.gpu_time_ms
    memory_reduction = (original_metrics.peak_memory_mb - compiled_metrics.peak_memory_mb) / original_metrics.peak_memory_mb * 100

    print(f"    Original: {original_metrics.gpu_time_ms:.2f} ms")
    print(f"    Compiled: {compiled_metrics.gpu_time_ms:.2f} ms")
    print(f"    Speedup: {speedup:.2f}x")
    print(f"    Memory change: {memory_reduction:+.1f}%")

    print("\n GPU profiling demonstration complete!")
    print("Key insights:")
    print("  • Comprehensive performance analysis across multiple dimensions")
    print("  • Layer-by-layer profiling for bottleneck identification")
    print("  • Implementation comparison for optimization selection")
    print("  • Automated optimization recommendations")
    print("  • torch.compile impact measurement")


if __name__ == "__main__":
    demonstrate_profiling_tools()
