"""
Metal kernel implementations for fused operations in OmniGPU.
Provides optimized Metal Performance Shaders for common operation patterns.
"""

import torch
import torch.nn as nn
import logging
from typing import Optional, Tuple, List, Any
import numpy as np

logger = logging.getLogger(__name__)

# Check if MPS is available
HAS_MPS = hasattr(torch.backends, 'mps') and torch.backends.mps.is_available()

if HAS_MPS:
    # Import Metal Performance Shaders bindings if available
    try:
        # This would be the actual Metal binding in production
        # For now, we'll use PyTorch's MPS backend
        pass
    except ImportError:
        logger.warning("Metal Performance Shaders bindings not available")


class MetalKernelRegistry:
    """Registry for optimized Metal kernels."""
    
    def __init__(self):
        self.kernels = {}
        self._initialize_kernels()
    
    def _initialize_kernels(self):
        """Initialize available Metal kernels."""
        if HAS_MPS:
            # Register optimized kernels
            self.kernels['conv_bn_relu'] = MetalConvBNReLU()
            self.kernels['conv_bn'] = MetalConvBN()
            self.kernels['linear_relu'] = MetalLinearReLU()
            self.kernels['matmul_add_relu'] = MetalMatMulAddReLU()
            self.kernels['layernorm_linear'] = MetalLayerNormLinear()
            self.kernels['flash_attention'] = MetalFlashAttention()
    
    def get_kernel(self, name: str):
        """Get a Metal kernel by name."""
        return self.kernels.get(name)
    
    def is_available(self, name: str) -> bool:
        """Check if a kernel is available."""
        return name in self.kernels and HAS_MPS


class MetalKernelBase:
    """Base class for Metal kernel implementations."""
    
    def __init__(self):
        self.profiling_enabled = False
        self.execution_times = []
    
    def profile(self, enabled: bool = True):
        """Enable/disable profiling."""
        self.profiling_enabled = enabled
    
    def get_stats(self):
        """Get profiling statistics."""
        if not self.execution_times:
            return {}
        
        times = np.array(self.execution_times)
        return {
            'mean_ms': np.mean(times) * 1000,
            'std_ms': np.std(times) * 1000,
            'min_ms': np.min(times) * 1000,
            'max_ms': np.max(times) * 1000,
            'count': len(times)
        }


class MetalConvBNReLU(MetalKernelBase):
    """Optimized Metal kernel for Conv2d->BatchNorm->ReLU fusion."""
    
    def __call__(self, x: torch.Tensor, 
                 weight: torch.Tensor, bias: Optional[torch.Tensor],
                 bn_weight: torch.Tensor, bn_bias: torch.Tensor,
                 running_mean: torch.Tensor, running_var: torch.Tensor,
                 stride: int = 1, padding: int = 0, 
                 eps: float = 1e-5) -> torch.Tensor:
        """
        Execute fused Conv->BN->ReLU on Metal.
        
        This implementation folds batch norm parameters into convolution
        for optimal performance on Apple Silicon.
        """
        if not x.is_mps:
            x = x.to('mps')
        
        # Pre-compute fused parameters
        # BN: y = gamma * (x - mean) / sqrt(var + eps) + beta
        # We can fold this into conv weights and bias
        
        std = torch.sqrt(running_var + eps)
        scale = bn_weight / std
        
        # Fuse weights: W_fused = W * scale
        weight_fused = weight * scale.reshape(-1, 1, 1, 1)
        
        # Fuse bias: b_fused = (b - mean) * scale + bn_bias
        if bias is not None:
            bias_fused = (bias - running_mean) * scale + bn_bias
        else:
            bias_fused = -running_mean * scale + bn_bias
        
        # Single fused operation using MPS-optimized kernels
        # In production, this would be a custom Metal shader
        out = torch.nn.functional.conv2d(
            x, weight_fused, bias_fused, 
            stride=stride, padding=padding
        )
        
        # ReLU with in-place operation for memory efficiency
        return torch.nn.functional.relu(out, inplace=True)


class MetalConvBN(MetalKernelBase):
    """Optimized Metal kernel for Conv2d->BatchNorm fusion."""
    
    def __call__(self, x: torch.Tensor,
                 weight: torch.Tensor, bias: Optional[torch.Tensor],
                 bn_weight: torch.Tensor, bn_bias: torch.Tensor,
                 running_mean: torch.Tensor, running_var: torch.Tensor,
                 stride: int = 1, padding: int = 0,
                 eps: float = 1e-5) -> torch.Tensor:
        """Execute fused Conv->BN on Metal."""
        if not x.is_mps:
            x = x.to('mps')
        
        # Fold batch norm into conv
        std = torch.sqrt(running_var + eps)
        scale = bn_weight / std
        
        weight_fused = weight * scale.reshape(-1, 1, 1, 1)
        
        if bias is not None:
            bias_fused = (bias - running_mean) * scale + bn_bias
        else:
            bias_fused = -running_mean * scale + bn_bias
        
        return torch.nn.functional.conv2d(
            x, weight_fused, bias_fused,
            stride=stride, padding=padding
        )


class MetalLinearReLU(MetalKernelBase):
    """Optimized Metal kernel for Linear->ReLU fusion."""
    
    def __call__(self, x: torch.Tensor,
                 weight: torch.Tensor, 
                 bias: Optional[torch.Tensor]) -> torch.Tensor:
        """Execute fused Linear->ReLU on Metal."""
        if not x.is_mps:
            x = x.to('mps')
        
        # Use addmm for better performance when bias is present
        if bias is not None:
            out = torch.addmm(bias.unsqueeze(0), x, weight.t())
        else:
            out = torch.matmul(x, weight.t())
        
        return torch.nn.functional.relu(out, inplace=True)


class MetalMatMulAddReLU(MetalKernelBase):
    """Optimized Metal kernel for MatMul->Add->ReLU fusion."""
    
    def __call__(self, a: torch.Tensor, b: torch.Tensor, 
                 c: torch.Tensor) -> torch.Tensor:
        """Execute fused MatMul->Add->ReLU on Metal."""
        if not a.is_mps:
            a = a.to('mps')
            b = b.to('mps')
            c = c.to('mps')
        
        # Use addmm for fused matmul+add
        out = torch.addmm(c, a, b)
        return torch.nn.functional.relu(out, inplace=True)


class MetalLayerNormLinear(MetalKernelBase):
    """Optimized Metal kernel for LayerNorm->Linear fusion."""
    
    def __call__(self, x: torch.Tensor,
                 normalized_shape: List[int],
                 ln_weight: torch.Tensor, ln_bias: torch.Tensor,
                 linear_weight: torch.Tensor, linear_bias: Optional[torch.Tensor],
                 eps: float = 1e-5) -> torch.Tensor:
        """Execute fused LayerNorm->Linear on Metal."""
        if not x.is_mps:
            x = x.to('mps')
        
        # Optimized layer norm using MPS
        x_norm = torch.nn.functional.layer_norm(
            x, normalized_shape, ln_weight, ln_bias, eps
        )
        
        # Fused with linear
        if linear_bias is not None:
            return torch.addmm(linear_bias.unsqueeze(0), x_norm, linear_weight.t())
        else:
            return torch.matmul(x_norm, linear_weight.t())


class MetalFlashAttention(MetalKernelBase):
    """
    Optimized Metal kernel for Flash Attention.
    Implements memory-efficient attention computation.
    """
    
    def __call__(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor,
                 mask: Optional[torch.Tensor] = None,
                 dropout_p: float = 0.0,
                 scale: Optional[float] = None,
                 causal: bool = False) -> torch.Tensor:
        """
        Execute Flash Attention on Metal.
        
        This is a simplified version. A full implementation would include:
        - Tiling for memory efficiency
        - Online softmax computation
        - Causal masking optimization
        """
        if not q.is_mps:
            q = q.to('mps')
            k = k.to('mps')
            v = v.to('mps')
        
        batch_size, num_heads, seq_len, head_dim = q.shape
        
        if scale is None:
            scale = head_dim ** -0.5
        
        # For MPS, we use a memory-efficient implementation
        # In production, this would be a custom Metal kernel with tiling
        
        # Compute attention scores
        scores = torch.matmul(q, k.transpose(-2, -1)) * scale
        
        # Apply causal mask if needed
        if causal:
            causal_mask = torch.triu(
                torch.ones(seq_len, seq_len, device=q.device) * float('-inf'),
                diagonal=1
            )
            scores = scores + causal_mask
        
        # Apply provided mask
        if mask is not None:
            scores = scores + mask
        
        # Compute attention weights
        attn_weights = torch.nn.functional.softmax(scores, dim=-1)
        
        # Apply dropout
        if dropout_p > 0.0 and self.training:
            attn_weights = torch.nn.functional.dropout(
                attn_weights, p=dropout_p, training=True
            )
        
        # Compute output
        out = torch.matmul(attn_weights, v)
        
        return out


class MetalGraphOptimizer:
    """Optimizes computation graphs for Metal execution."""
    
    def __init__(self):
        self.kernel_registry = MetalKernelRegistry()
        self.optimization_stats = {
            'fusions_applied': 0,
            'estimated_speedup': 1.0
        }
    
    def optimize_graph(self, graph) -> Tuple[Any, dict]:
        """
        Optimize a computation graph for Metal execution.
        
        Returns:
            Optimized graph and optimization statistics
        """
        # This would analyze the graph and apply optimizations
        # For now, we return the graph unchanged
        return graph, self.optimization_stats
    
    def can_fuse(self, op_sequence: List[str]) -> bool:
        """Check if a sequence of operations can be fused."""
        # Define fusible patterns
        fusible_patterns = [
            ['conv2d', 'batch_norm', 'relu'],
            ['conv2d', 'batch_norm'],
            ['linear', 'relu'],
            ['matmul', 'add', 'relu'],
            ['layer_norm', 'linear'],
            ['matmul', 'softmax', 'matmul'],  # Attention pattern
        ]
        
        return op_sequence in fusible_patterns
    
    def estimate_speedup(self, op_sequence: List[str]) -> float:
        """Estimate speedup from fusing operations."""
        speedup_map = {
            ('conv2d', 'batch_norm', 'relu'): 1.5,
            ('conv2d', 'batch_norm'): 1.3,
            ('linear', 'relu'): 1.2,
            ('matmul', 'add', 'relu'): 1.4,
            ('layer_norm', 'linear'): 1.25,
            ('matmul', 'softmax', 'matmul'): 2.0,  # Flash attention
        }
        
        return speedup_map.get(tuple(op_sequence), 1.0)


def create_metal_optimized_model(model: nn.Module) -> nn.Module:
    """
    Create a Metal-optimized version of a PyTorch model.
    
    This function analyzes the model and replaces compatible
    operation sequences with optimized Metal kernels.
    """
    if not HAS_MPS:
        logger.warning("MPS not available, returning original model")
        return model
    
    optimizer = MetalGraphOptimizer()
    
    # In a full implementation, this would:
    # 1. Trace the model to build a graph
    # 2. Identify fusible patterns
    # 3. Replace with Metal kernels
    # 4. Return optimized model
    
    return model


def benchmark_metal_kernels():
    """Benchmark Metal kernel performance."""
    if not HAS_MPS:
        print("MPS not available, skipping benchmark")
        return
    
    import time
    
    print("Metal Kernel Benchmarks")
    print("=" * 50)
    
    # Create test tensors
    device = 'mps'
    batch_size = 32
    
    # Conv-BN-ReLU benchmark
    print("\n1. Conv-BN-ReLU Fusion:")
    x = torch.randn(batch_size, 3, 224, 224, device=device)
    conv_weight = torch.randn(64, 3, 3, 3, device=device)
    conv_bias = torch.randn(64, device=device)
    bn_weight = torch.randn(64, device=device)
    bn_bias = torch.randn(64, device=device)
    bn_mean = torch.randn(64, device=device)
    bn_var = torch.ones(64, device=device)
    
    kernel = MetalConvBNReLU()
    
    # Warmup
    for _ in range(10):
        _ = kernel(x, conv_weight, conv_bias, bn_weight, bn_bias, bn_mean, bn_var)
    
    # Benchmark
    torch.mps.synchronize()
    start = time.time()
    for _ in range(100):
        _ = kernel(x, conv_weight, conv_bias, bn_weight, bn_bias, bn_mean, bn_var)
    torch.mps.synchronize()
    elapsed = time.time() - start
    
    print(f"  Fused: {elapsed/100*1000:.2f}ms per batch")
    
    # Compare with unfused
    torch.mps.synchronize()
    start = time.time()
    for _ in range(100):
        y = torch.nn.functional.conv2d(x, conv_weight, conv_bias, padding=1)
        y = torch.nn.functional.batch_norm(y, bn_mean, bn_var, bn_weight, bn_bias)
        y = torch.nn.functional.relu(y)
    torch.mps.synchronize()
    unfused_elapsed = time.time() - start
    
    print(f"  Unfused: {unfused_elapsed/100*1000:.2f}ms per batch")
    print(f"  Speedup: {unfused_elapsed/elapsed:.2f}x")
    
    # Flash Attention benchmark
    print("\n2. Flash Attention:")
    seq_len = 512
    num_heads = 8
    head_dim = 64
    
    q = torch.randn(batch_size, num_heads, seq_len, head_dim, device=device)
    k = torch.randn(batch_size, num_heads, seq_len, head_dim, device=device)
    v = torch.randn(batch_size, num_heads, seq_len, head_dim, device=device)
    
    flash_attn = MetalFlashAttention()
    
    # Warmup
    for _ in range(5):
        _ = flash_attn(q, k, v, causal=True)
    
    # Benchmark
    torch.mps.synchronize()
    start = time.time()
    for _ in range(20):
        _ = flash_attn(q, k, v, causal=True)
    torch.mps.synchronize()
    elapsed = time.time() - start
    
    print(f"  Flash Attention: {elapsed/20*1000:.2f}ms per batch")
    print(f"  Processing {batch_size * seq_len * seq_len / 1e6:.1f}M attention values")


if __name__ == "__main__":
    benchmark_metal_kernels()