"""
Memory Efficiency Optimization Patterns for GPU Computing

This module provides comprehensive guidance and practical implementations for
optimizing GPU memory utilization in PyTorch neural networks, focusing on
bandwidth optimization, allocation strategies, and cache-friendly patterns.

GPU memory hierarchy optimization is critical for high-performance computing:
- Global memory bandwidth: Often the primary bottleneck (500-1000 GB/s)
- L2 cache: 40MB shared across GPU, crucial for data reuse
- L1 cache: 128KB per SM, optimized for spatial/temporal locality
- Shared memory: 48-164KB per SM, programmer-controlled fast storage
- Registers: 65K 32-bit registers per SM, fastest memory tier

Learn to identify memory bottlenecks in your models and apply proven patterns
that improve throughput through better memory utilization.
"""

from collections.abc import Callable
from dataclasses import dataclass
from enum import Enum
from typing import Any

import torch
import torch.nn as nn


class MemoryAccessPattern(Enum):
    """Types of memory access patterns for optimization analysis."""
    COALESCED = "coalesced"
    STRIDED = "strided"
    RANDOM = "random"
    BROADCAST = "broadcast"
    REDUCTION = "reduction"


@dataclass
class MemoryOptimizationStrategy:
    """
    Data structure for describing memory optimization patterns.

    By categorizing memory optimization opportunities, we can:
    - Systematically identify memory bottlenecks
    - Apply proven optimization strategies
    - Measure and validate memory efficiency improvements
    - Build reusable optimization patterns
    """
    name: str
    access_pattern: MemoryAccessPattern
    techniques: list[str]
    bandwidth_improvement: float  # Expected bandwidth utilization improvement
    memory_reduction: float  # Expected memory usage reduction (0.0-1.0)
    cache_efficiency: float  # Expected cache hit ratio improvement
    description: str
    example_before: str
    example_after: str


#  EDUCATIONAL: Common memory optimization patterns in neural networks
MEMORY_OPTIMIZATION_GUIDE = [
    MemoryOptimizationStrategy(
        name="Tensor Reshaping for Coalescing",
        access_pattern=MemoryAccessPattern.COALESCED,
        techniques=["Contiguous layout", "Memory coalescing", "Stride optimization"],
        bandwidth_improvement=0.8,
        memory_reduction=0.0,
        cache_efficiency=0.6,
        description="Reorganize tensor access patterns for optimal memory coalescing",
        example_before="x = x.transpose(-1, -2)  # Non-contiguous access",
        example_after="x = x.transpose(-1, -2).contiguous()  # Coalesced access"
    ),

    MemoryOptimizationStrategy(
        name="In-Place Operations",
        access_pattern=MemoryAccessPattern.COALESCED,
        techniques=["In-place updates", "Memory reuse", "Allocation elimination"],
        bandwidth_improvement=0.4,
        memory_reduction=0.5,
        cache_efficiency=0.3,
        description="Use in-place operations to eliminate memory allocations",
        example_before="x = F.relu(x)  # Allocates new tensor",
        example_after="x.relu_()  # In-place, no allocation"
    ),

    MemoryOptimizationStrategy(
        name="Tensor Layout Optimization",
        access_pattern=MemoryAccessPattern.COALESCED,
        techniques=["Channel-last format", "NHWC layout", "Memory stride optimization"],
        bandwidth_improvement=0.7,
        memory_reduction=0.0,
        cache_efficiency=0.8,
        description="Optimize tensor memory layout for GPU cache efficiency",
        example_before="x = x.to(memory_format=torch.contiguous_format)",
        example_after="x = x.to(memory_format=torch.channels_last)"
    ),

    MemoryOptimizationStrategy(
        name="Gradient Checkpointing",
        access_pattern=MemoryAccessPattern.REDUCTION,
        techniques=["Activation recomputation", "Memory-compute tradeoff"],
        bandwidth_improvement=0.0,
        memory_reduction=0.6,
        cache_efficiency=0.0,
        description="Trade computation for memory in large model training",
        example_before="# Standard forward pass stores all activations",
        example_after="torch.utils.checkpoint.checkpoint(layer, x)"
    ),

    MemoryOptimizationStrategy(
        name="Shared Memory Optimization",
        access_pattern=MemoryAccessPattern.COALESCED,
        techniques=["Block-wise processing", "Tile-based computation", "Data locality"],
        bandwidth_improvement=0.9,
        memory_reduction=0.2,
        cache_efficiency=0.9,
        description="Optimize for GPU shared memory utilization",
        example_before="# Standard global memory access",
        example_after="# Custom kernels with shared memory tiling"
    )
]


def analyze_memory_access_patterns(
    model: nn.Module,
    sample_input: torch.Tensor,
    batch_sizes: list[int] = [1, 8, 32, 128]  # noqa: B006
) -> dict[str, Any]:
    """
    Analyze memory access patterns and identify optimization opportunities.

    This function demonstrates how to systematically analyze memory usage patterns
    to identify optimization opportunities. It serves as a template for building
    memory optimization analysis tools.

    Args:
        model: PyTorch model to analyze
        sample_input: Representative input tensor for analysis
        batch_sizes: Different batch sizes for scaling analysis

    Returns:
        Comprehensive analysis of memory usage patterns and optimization opportunities
    """
    analysis_results = {
        "memory_usage_by_batch": {},
        "allocation_hotspots": [],
        "access_patterns": {},
        "optimization_opportunities": [],
        "bandwidth_utilization": {}
    }

    for batch_size in batch_sizes:
        if sample_input.shape[0] == 1:
            # Single sample input - can expand to different batch sizes
            batch_input = sample_input.expand(batch_size, *sample_input.shape[1:])
        else:
            # Multi-sample input - create new tensor with target batch size
            single_sample = sample_input[:1]  # Take first sample
            batch_input = single_sample.expand(batch_size, *single_sample.shape[1:])

        # Measure peak memory usage
        torch.cuda.reset_peak_memory_stats() if torch.cuda.is_available() else None

        with torch.no_grad():
            model(batch_input)

        peak_memory = torch.cuda.max_memory_allocated() if torch.cuda.is_available() else 0
        analysis_results["memory_usage_by_batch"][batch_size] = {
            "peak_memory_mb": peak_memory / (1024**2),
            "memory_per_sample": peak_memory / batch_size if batch_size > 0 else 0
        }

    analysis_results["module_analysis"] = _analyze_module_memory_patterns(model)

    analysis_results["optimization_opportunities"] = _identify_memory_optimizations(model, sample_input)

    analysis_results["bandwidth_analysis"] = _analyze_memory_bandwidth(model, sample_input)

    return analysis_results


def _analyze_module_memory_patterns(model: nn.Module) -> dict[str, Any]:
    """Analyze memory patterns for individual modules."""
    module_patterns = {}

    for name, module in model.named_modules():
        if len(list(module.children())) == 0:  # Leaf modules only
            # Classify module memory characteristics
            if isinstance(module, (nn.Linear, nn.Conv2d)):
                module_patterns[name] = {
                    "type": "compute_intensive",
                    "memory_pattern": "weight_dominated",
                    "optimization_potential": "high"
                }
            elif isinstance(module, (nn.LayerNorm, nn.BatchNorm2d)):
                module_patterns[name] = {
                    "type": "bandwidth_intensive",
                    "memory_pattern": "activation_dominated",
                    "optimization_potential": "medium"
                }
            elif isinstance(module, (nn.ReLU, nn.GELU, nn.SiLU)):
                module_patterns[name] = {
                    "type": "element_wise",
                    "memory_pattern": "streaming",
                    "optimization_potential": "fusion_candidate"
                }

    return module_patterns


def _identify_memory_optimizations(model: nn.Module, sample_input: torch.Tensor) -> list[dict[str, Any]]:
    """Identify specific memory optimization opportunities."""
    optimizations = []

    # Check for non-contiguous tensor operations
    modules = list(model.named_modules())
    for _unused_i, (name, module) in enumerate(modules):
        if hasattr(module, 'weight') and hasattr(module.weight, 'is_contiguous'):
            if not module.weight.is_contiguous():
                optimizations.append({
                    "type": "non_contiguous_weights",
                    "module": name,
                    "recommendation": "Use .contiguous() or redesign weight initialization",
                    "potential_speedup": 1.2
                })

        # Check for transpose operations that could benefit from layout optimization
        if 'transpose' in name.lower() or 'permute' in name.lower():
            optimizations.append({
                "type": "transpose_operation",
                "module": name,
                "recommendation": "Consider channels_last format or fused operations",
                "potential_speedup": 1.5
            })

    # Check for activation functions that could be fused
    for i, (name, module) in enumerate(modules[:-1]):
        next_name, next_module = modules[i + 1]
        if isinstance(module, nn.Linear) and isinstance(next_module, (nn.ReLU, nn.GELU, nn.SiLU)):
            optimizations.append({
                "type": "fusion_opportunity",
                "modules": [name, next_name],
                "recommendation": "Use fused linear-activation implementation",
                "potential_speedup": 1.8
            })

    return optimizations


def _analyze_memory_bandwidth(model: nn.Module, sample_input: torch.Tensor) -> dict[str, Any]:
    """Analyze memory bandwidth utilization characteristics."""
    # Educational implementation - in practice would use profiling tools
    total_params = sum(p.numel() for p in model.parameters())
    total_param_memory = total_params * 4  # Assuming fp32

    input_memory = sample_input.numel() * sample_input.element_size()

    # Estimate arithmetic intensity (FLOPs per byte)
    estimated_flops = _estimate_model_flops(model, sample_input)
    total_memory_access = total_param_memory + input_memory
    arithmetic_intensity = estimated_flops / total_memory_access if total_memory_access > 0 else 0

    return {
        "arithmetic_intensity": arithmetic_intensity,
        "parameter_memory_gb": total_param_memory / (1024**3),
        "activation_memory_estimate_gb": input_memory / (1024**3),
        "bandwidth_bound": arithmetic_intensity < 10,  # Rule of thumb
        "optimization_priority": "memory" if arithmetic_intensity < 10 else "compute"
    }


def _estimate_model_flops(model: nn.Module, sample_input: torch.Tensor) -> float:
    """Rough estimation of model FLOPs for educational purposes."""
    # Educational placeholder - real implementation would use detailed FLOP counting
    total_flops = 0
    for module in model.modules():
        if isinstance(module, nn.Linear):
            total_flops += 2 * module.in_features * module.out_features
        elif isinstance(module, nn.Conv2d):
            # Simplified FLOP count for convolution
            kernel_flops = module.kernel_size[0] * module.kernel_size[1] * module.in_channels
            output_elements = sample_input.shape[-2] * sample_input.shape[-1]  # Simplified
            total_flops += kernel_flops * output_elements * module.out_channels
    return total_flops


def optimize_tensor_layouts(
    model: nn.Module,
    optimization_strategy: str = "channels_last",
    target_device: str = "cuda"
) -> nn.Module:
    """
    Optimize tensor layouts for improved memory access patterns.

    Memory layout significantly impacts GPU performance. This function demonstrates
    how different layout strategies can improve cache efficiency and memory bandwidth.

    Args:
        model: Model to optimize
        optimization_strategy: Layout optimization strategy
        target_device: Target device for optimization

    Returns:
        Model with optimized tensor layouts
    """
    optimized_model = model.to(target_device)

    if optimization_strategy == "channels_last":
        #  OPTIMIZATION: Convert to channels_last format for convolutions
        for module in optimized_model.modules():
            if isinstance(module, (nn.Conv2d, nn.BatchNorm2d)):
                if hasattr(module, 'weight'):
                    module.weight.data = module.weight.data.to(memory_format=torch.channels_last)
                if hasattr(module, 'bias') and module.bias is not None:
                    module.bias.data = module.bias.data.contiguous()

    elif optimization_strategy == "contiguous":
        #  OPTIMIZATION: Ensure all parameters are contiguous
        for param in optimized_model.parameters():
            param.data = param.data.contiguous()

    return optimized_model


def minimize_memory_allocations(
    operations: list[Callable],
    input_tensor: torch.Tensor,
    use_inplace: bool = True
) -> torch.Tensor:
    """
    Minimize memory allocations through operation optimization.

    GPU memory allocation/deallocation overhead can be significant, especially
    for small tensors. This function demonstrates techniques to minimize
    memory churn and improve performance.

    Args:
        operations: List of operations to apply with minimal allocations
        input_tensor: Input tensor to process
        use_inplace: Whether to use in-place operations when possible

    Returns:
        Processed tensor with minimal memory allocations
    """
    if use_inplace:
        #  OPTIMIZATION: In-place operation chain
        result = input_tensor.clone()  # Single allocation
        for op in operations:
            if hasattr(op, '__name__') and 'relu' in op.__name__:
                result.relu_()  # In-place ReLU
            elif hasattr(op, '__name__') and 'add' in op.__name__:
                # Would need additional context for in-place add
                result = op(result)
            else:
                result = op(result)
    else:
        # Standard operation chain (creates intermediate tensors)
        result = input_tensor
        for op in operations:
            result = op(result)

    return result


class MemoryEfficientSequential(nn.Module):
    """
    Memory-efficient sequential module with optimization patterns.

    Standard nn.Sequential can be memory-inefficient for large models.
    This implementation demonstrates memory optimization techniques for
    sequential neural network architectures.

    """

    def __init__(self, *modules, use_checkpoint: bool = False, checkpoint_segments: int = 1):
        """
        Initialize memory-efficient sequential module.

        Args:
            *modules: Sequential modules to process
            use_checkpoint: Whether to use gradient checkpointing
            checkpoint_segments: Number of checkpointing segments
        """
        super().__init__()
        self.modules_list = nn.ModuleList(modules)
        self.use_checkpoint = use_checkpoint
        self.checkpoint_segments = checkpoint_segments

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Memory-efficient forward pass with optional checkpointing.

        """
        if self.use_checkpoint and self.training:
            #  MEMORY OPTIMIZATION: Gradient checkpointing
            return self._checkpointed_forward(x)
        else:
            return self._standard_forward(x)

    def _standard_forward(self, x: torch.Tensor) -> torch.Tensor:
        """Standard forward pass without checkpointing."""
        for module in self.modules_list:
            x = module(x)
        return x

    def _checkpointed_forward(self, x: torch.Tensor) -> torch.Tensor:
        """Forward pass with gradient checkpointing for memory efficiency."""
        if self.checkpoint_segments == 1:
            # Single checkpoint for entire sequence
            return torch.utils.checkpoint.checkpoint(
                self._standard_forward, x, use_reentrant=False
            )
        else:
            # Segmented checkpointing
            segment_size = len(self.modules_list) // self.checkpoint_segments
            for i in range(0, len(self.modules_list), segment_size):
                segment_modules = self.modules_list[i:i + segment_size]
                segment_sequential = nn.Sequential(*segment_modules)
                x = torch.utils.checkpoint.checkpoint(
                    segment_sequential, x, use_reentrant=False
                )
            return x


class AdaptiveMemoryManager:
    """
    Adaptive memory manager for dynamic memory optimization.

    Different models and workloads have different memory characteristics.
    This class demonstrates adaptive strategies that adjust to runtime
    memory patterns for optimal performance.
    """

    def __init__(self, initial_strategy: str = "balanced"):
        self.strategy = initial_strategy
        self.memory_stats = {
            "peak_usage": 0,
            "allocation_count": 0,
            "fragmentation_ratio": 0
        }

    def optimize_for_batch_size(self, model: nn.Module, batch_size: int) -> dict[str, Any]:
        """
        Optimize memory strategy based on batch size characteristics.

        """
        if batch_size <= 4:
            # Small batch optimization
            return {
                "strategy": "minimal_allocation",
                "use_inplace": True,
                "checkpoint_layers": False,
                "memory_format": torch.contiguous_format
            }
        elif batch_size <= 32:
            # Medium batch optimization
            return {
                "strategy": "balanced",
                "use_inplace": False,
                "checkpoint_layers": True,
                "memory_format": torch.channels_last
            }
        else:
            # Large batch optimization
            return {
                "strategy": "throughput_optimized",
                "use_inplace": False,
                "checkpoint_layers": True,
                "memory_format": torch.channels_last
            }

    def analyze_memory_pressure(self) -> dict[str, Any]:
        """Analyze current memory pressure and recommend optimizations."""
        if torch.cuda.is_available():
            current_memory = torch.cuda.memory_allocated()
            peak_memory = torch.cuda.max_memory_allocated()
            total_memory = torch.cuda.get_device_properties(0).total_memory

            memory_pressure = current_memory / total_memory
            fragmentation_estimate = 1.0 - (current_memory / peak_memory) if peak_memory > 0 else 0

            return {
                "memory_pressure": memory_pressure,
                "fragmentation_ratio": fragmentation_estimate,
                "optimization_urgency": memory_pressure,
                "recommended_action": "aggressive_optimization" if memory_pressure > 0.8 else "standard"
            }

        return {"memory_pressure": 0, "fragmentation_ratio": 0, "optimization_urgency": 0}


#  EDUCATIONAL: Memory optimization utility functions
def print_memory_analysis(analysis_results: dict[str, Any]) -> None:
    """
    Print memory analysis results in a readable format.

    This demonstrates how to present memory analysis results in a way that's
    actionable for developers optimizing GPU memory usage.
    """
    print(" Memory Usage Analysis Results\n")

    # Memory usage by batch size
    if "memory_usage_by_batch" in analysis_results:
        print(" Memory Usage by Batch Size:")
        for batch_size, stats in analysis_results["memory_usage_by_batch"].items():
            print(f"  Batch {batch_size:3d}: {stats['peak_memory_mb']:.1f} MB total, "
                  f"{stats['memory_per_sample']:.1f} MB/sample")
        print()

    # Optimization opportunities
    if "optimization_opportunities" in analysis_results:
        opportunities = analysis_results["optimization_opportunities"]
        print(f" Found {len(opportunities)} Memory Optimization Opportunities:")
        for i, opp in enumerate(opportunities, 1):
            print(f"  {i}. {opp.get('type', 'Unknown')}")
            print(f"     Module(s): {opp.get('module', opp.get('modules', 'N/A'))}")
            print(f"     Potential speedup: {opp.get('potential_speedup', 'N/A')}x")
            print(f"     Recommendation: {opp.get('recommendation', 'N/A')}")
        print()

    # Bandwidth analysis
    if "bandwidth_analysis" in analysis_results:
        bandwidth = analysis_results["bandwidth_analysis"]
        print(" Memory Bandwidth Analysis:")
        print(f"  Arithmetic intensity: {bandwidth.get('arithmetic_intensity', 0):.2f} FLOP/byte")
        print(f"  Parameter memory: {bandwidth.get('parameter_memory_gb', 0):.2f} GB")
        print(f"  Optimization priority: {bandwidth.get('optimization_priority', 'unknown')}")
        print(f"  Bandwidth bound: {'Yes' if bandwidth.get('bandwidth_bound', False) else 'No'}")


def benchmark_memory_optimizations(
    original_model: nn.Module,
    optimized_model: nn.Module,
    sample_input: torch.Tensor,
    num_iterations: int = 50
) -> dict[str, float]:
    """
    Benchmark memory optimization impact.

    Always measure memory optimization impact to verify that theoretical
    improvements translate to real memory efficiency gains.
    """
    # Educational implementation - would use detailed memory profiling in practice
    return {
        "memory_reduction_ratio": 0.4,  # Educational placeholder
        "peak_memory_improvement": 0.6,
        "allocation_count_reduction": 0.5,
        "bandwidth_utilization_improvement": 0.3
    }
