"""
CUDA-accelerated triangle operations for OpenFold++.

This module provides CUDA-optimized implementations of triangle attention
and triangle multiplication operations used in the Evoformer.
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple
import logging

# Try to import CUDA kernels
try:
    import openfold_cuda_kernels
    CUDA_KERNELS_AVAILABLE = True
    logging.info("OpenFold++ CUDA kernels loaded successfully")
except ImportError:
    CUDA_KERNELS_AVAILABLE = False
    logging.warning("OpenFold++ CUDA kernels not available, falling back to PyTorch implementation")


class CudaTriangleAttention(nn.Module):
    """
    CUDA-accelerated triangle attention module.
    
    This replaces the standard triangle attention with optimized CUDA kernels
    for significant speedup on GPU.
    """
    
    def __init__(self, 
                 c_in: int,
                 c_hidden: int, 
                 no_heads: int,
                 starting: bool = True,
                 inf: float = 1e9):
        """
        Args:
            c_in: Input channel dimension
            c_hidden: Hidden channel dimension  
            no_heads: Number of attention heads
            starting: Whether this is starting node attention
            inf: Large value for masking
        """
        super(CudaTriangleAttention, self).__init__()
        
        self.c_in = c_in
        self.c_hidden = c_hidden
        self.no_heads = no_heads
        self.starting = starting
        self.inf = inf
        
        # Linear projections
        self.linear_q = nn.Linear(c_in, c_hidden * no_heads, bias=False)
        self.linear_k = nn.Linear(c_in, c_hidden * no_heads, bias=False)
        self.linear_v = nn.Linear(c_in, c_hidden * no_heads, bias=False)
        self.linear_o = nn.Linear(c_hidden * no_heads, c_in)
        
        # Gating
        self.linear_g = nn.Linear(c_in, c_hidden * no_heads)
        self.sigmoid = nn.Sigmoid()
        
        # Layer norm
        self.layer_norm = nn.LayerNorm(c_in)
        
    def forward(self, 
                x: torch.Tensor,
                mask: Optional[torch.Tensor] = None,
                chunk_size: Optional[int] = None) -> torch.Tensor:
        """
        Forward pass of triangle attention.
        
        Args:
            x: Input tensor [batch, seq_i, seq_j, c_in]
            mask: Optional mask tensor [batch, seq_i, seq_j]
            chunk_size: Optional chunking for memory efficiency
            
        Returns:
            Output tensor [batch, seq_i, seq_j, c_in]
        """
        batch_size, seq_i, seq_j, c_in = x.shape
        
        # Apply layer norm
        x_norm = self.layer_norm(x)
        
        # Linear projections
        q = self.linear_q(x_norm)  # [batch, seq_i, seq_j, c_hidden * no_heads]
        k = self.linear_k(x_norm)
        v = self.linear_v(x_norm)
        
        # Reshape for multi-head attention
        q = q.view(batch_size, seq_i, seq_j, self.no_heads, self.c_hidden)
        k = k.view(batch_size, seq_i, seq_j, self.no_heads, self.c_hidden)
        v = v.view(batch_size, seq_i, seq_j, self.no_heads, self.c_hidden)
        
        # Transpose to [batch, no_heads, seq_i, seq_j, c_hidden]
        q = q.transpose(2, 3).transpose(1, 2)
        k = k.transpose(2, 3).transpose(1, 2)
        v = v.transpose(2, 3).transpose(1, 2)
        
        # Prepare bias tensors
        bias_mask = torch.zeros(batch_size, seq_i, 1, 1, seq_j, device=x.device, dtype=x.dtype)
        if mask is not None:
            # Convert mask to bias (0 for valid, -inf for invalid)
            mask_bias = torch.where(mask.unsqueeze(2).unsqueeze(3), 0.0, -self.inf)
            bias_mask = mask_bias.unsqueeze(1)  # Add head dimension
        
        # Triangle bias (simplified - in real implementation this would be more complex)
        triangle_bias = torch.zeros(batch_size, 1, self.no_heads, seq_i, seq_j, 
                                   device=x.device, dtype=x.dtype)
        
        # Use CUDA kernel if available
        if CUDA_KERNELS_AVAILABLE and x.is_cuda:
            try:
                # Call CUDA kernel
                attn_output = openfold_cuda_kernels.triangle_attention_forward(
                    q, k, v, bias_mask, triangle_bias, self.starting
                )
            except Exception as e:
                logging.warning(f"CUDA kernel failed, falling back to PyTorch: {e}")
                attn_output = self._pytorch_attention(q, k, v, bias_mask, triangle_bias)
        else:
            # Fallback to PyTorch implementation
            attn_output = self._pytorch_attention(q, k, v, bias_mask, triangle_bias)
        
        # Reshape back to [batch, seq_i, seq_j, c_hidden * no_heads]
        attn_output = attn_output.transpose(1, 2).transpose(2, 3)
        attn_output = attn_output.contiguous().view(batch_size, seq_i, seq_j, -1)
        
        # Apply gating
        g = self.sigmoid(self.linear_g(x_norm))
        attn_output = attn_output * g
        
        # Final linear projection
        output = self.linear_o(attn_output)
        
        # Residual connection
        return x + output
    
    def _pytorch_attention(self, 
                          q: torch.Tensor, 
                          k: torch.Tensor, 
                          v: torch.Tensor,
                          bias_mask: torch.Tensor,
                          triangle_bias: torch.Tensor) -> torch.Tensor:
        """
        PyTorch fallback implementation of triangle attention.
        """
        # Compute attention scores
        scores = torch.matmul(q, k.transpose(-2, -1)) / (self.c_hidden ** 0.5)
        
        # Add biases
        scores = scores + bias_mask + triangle_bias
        
        # Apply softmax
        attn_weights = F.softmax(scores, dim=-1)
        
        # Apply attention to values
        output = torch.matmul(attn_weights, v)
        
        return output


class CudaTriangleMultiplication(nn.Module):
    """
    CUDA-accelerated triangle multiplication module.
    """
    
    def __init__(self, c_in: int, c_hidden: int, outgoing: bool = True):
        """
        Args:
            c_in: Input channel dimension
            c_hidden: Hidden channel dimension
            outgoing: Whether this is outgoing or incoming multiplication
        """
        super(CudaTriangleMultiplication, self).__init__()
        
        self.c_in = c_in
        self.c_hidden = c_hidden
        self.outgoing = outgoing
        
        # Linear projections
        self.linear_a_p = nn.Linear(c_in, c_hidden)
        self.linear_a_g = nn.Linear(c_in, c_hidden)
        self.linear_b_p = nn.Linear(c_in, c_hidden)
        self.linear_b_g = nn.Linear(c_in, c_hidden)
        self.linear_z = nn.Linear(c_hidden, c_in)
        
        # Layer norm
        self.layer_norm = nn.LayerNorm(c_in)
        
        # Activation functions
        self.sigmoid = nn.Sigmoid()
        
    def forward(self, 
                x: torch.Tensor,
                mask: Optional[torch.Tensor] = None) -> torch.Tensor:
        """
        Forward pass of triangle multiplication.
        
        Args:
            x: Input tensor [batch, seq_i, seq_j, c_in]
            mask: Optional mask tensor [batch, seq_i, seq_j]
            
        Returns:
            Output tensor [batch, seq_i, seq_j, c_in]
        """
        # Apply layer norm
        x_norm = self.layer_norm(x)
        
        # Prepare mask
        if mask is None:
            mask = torch.ones(x.shape[:-1], device=x.device, dtype=x.dtype)
        
        # Expand mask to match input dimensions
        mask_expanded = mask.unsqueeze(-1)  # [batch, seq_i, seq_j, 1]
        
        # Use CUDA kernel if available
        if CUDA_KERNELS_AVAILABLE and x.is_cuda:
            try:
                # Call CUDA kernel
                output = openfold_cuda_kernels.triangle_multiply_forward(
                    x_norm, mask_expanded, self.outgoing
                )
            except Exception as e:
                logging.warning(f"CUDA kernel failed, falling back to PyTorch: {e}")
                output = self._pytorch_triangle_multiply(x_norm, mask_expanded)
        else:
            # Fallback to PyTorch implementation
            output = self._pytorch_triangle_multiply(x_norm, mask_expanded)
        
        # Residual connection
        return x + output
    
    def _pytorch_triangle_multiply(self, 
                                  x: torch.Tensor, 
                                  mask: torch.Tensor) -> torch.Tensor:
        """
        PyTorch fallback implementation of triangle multiplication.
        """
        # Linear projections
        a_p = self.linear_a_p(x)  # [batch, seq_i, seq_j, c_hidden]
        a_g = self.sigmoid(self.linear_a_g(x))
        b_p = self.linear_b_p(x)
        b_g = self.sigmoid(self.linear_b_g(x))
        
        # Apply gating
        a = a_p * a_g
        b = b_p * b_g
        
        # Apply mask
        a = a * mask
        b = b * mask
        
        # Triangle multiplication
        if self.outgoing:
            # Outgoing: sum over k dimension
            # z_ij = sum_k(a_ik * b_kj)
            output = torch.einsum('bikc,bkjc->bijc', a, b)
        else:
            # Incoming: sum over k dimension (different indexing)
            # z_ij = sum_k(a_ki * b_kj)  
            output = torch.einsum('bkic,bkjc->bijc', a, b)
        
        # Final linear projection
        output = self.linear_z(output)
        
        # Apply mask to output
        output = output * mask
        
        return output


def replace_triangle_ops_with_cuda(model: nn.Module) -> nn.Module:
    """
    Replace standard triangle operations with CUDA-accelerated versions.
    
    Args:
        model: OpenFold model to modify
        
    Returns:
        Modified model with CUDA triangle operations
    """
    if not CUDA_KERNELS_AVAILABLE:
        logging.warning("CUDA kernels not available, cannot replace triangle operations")
        return model
    
    # This would recursively replace triangle attention and multiplication modules
    # Implementation depends on the specific structure of the OpenFold model
    
    def replace_modules(module):
        for name, child in module.named_children():
            if hasattr(child, '__class__') and 'TriangleAttention' in child.__class__.__name__:
                # Replace with CUDA version
                cuda_module = CudaTriangleAttention(
                    c_in=getattr(child, 'c_in', 256),
                    c_hidden=getattr(child, 'c_hidden', 32),
                    no_heads=getattr(child, 'no_heads', 8),
                    starting=getattr(child, 'starting', True)
                )
                setattr(module, name, cuda_module)
                logging.info(f"Replaced {name} with CUDA triangle attention")
                
            elif hasattr(child, '__class__') and 'TriangleMultiplication' in child.__class__.__name__:
                # Replace with CUDA version
                cuda_module = CudaTriangleMultiplication(
                    c_in=getattr(child, 'c_in', 256),
                    c_hidden=getattr(child, 'c_hidden', 128),
                    outgoing=getattr(child, 'outgoing', True)
                )
                setattr(module, name, cuda_module)
                logging.info(f"Replaced {name} with CUDA triangle multiplication")
            else:
                # Recursively process child modules
                replace_modules(child)
    
    replace_modules(model)
    return model


# Utility functions
def benchmark_triangle_ops(batch_size: int = 2, 
                          seq_len: int = 256, 
                          channels: int = 256,
                          num_iterations: int = 10) -> dict:
    """
    Benchmark CUDA vs PyTorch triangle operations.
    
    Returns:
        Dictionary with timing results
    """
    if not torch.cuda.is_available():
        return {"error": "CUDA not available"}
    
    device = torch.device("cuda")
    
    # Create test tensors
    x = torch.randn(batch_size, seq_len, seq_len, channels, device=device)
    mask = torch.ones(batch_size, seq_len, seq_len, device=device)
    
    results = {}
    
    # Benchmark CUDA triangle attention
    if CUDA_KERNELS_AVAILABLE:
        cuda_attn = CudaTriangleAttention(channels, 32, 8).to(device)
        
        # Warmup
        for _ in range(3):
            _ = cuda_attn(x, mask)
        
        torch.cuda.synchronize()
        start_time = torch.cuda.Event(enable_timing=True)
        end_time = torch.cuda.Event(enable_timing=True)
        
        start_time.record()
        for _ in range(num_iterations):
            _ = cuda_attn(x, mask)
        end_time.record()
        
        torch.cuda.synchronize()
        cuda_time = start_time.elapsed_time(end_time) / num_iterations
        results['cuda_attention_ms'] = cuda_time
    
    # Benchmark CUDA triangle multiplication
    if CUDA_KERNELS_AVAILABLE:
        cuda_mult = CudaTriangleMultiplication(channels, 128).to(device)
        
        # Warmup
        for _ in range(3):
            _ = cuda_mult(x, mask)
        
        torch.cuda.synchronize()
        start_time = torch.cuda.Event(enable_timing=True)
        end_time = torch.cuda.Event(enable_timing=True)
        
        start_time.record()
        for _ in range(num_iterations):
            _ = cuda_mult(x, mask)
        end_time.record()
        
        torch.cuda.synchronize()
        cuda_mult_time = start_time.elapsed_time(end_time) / num_iterations
        results['cuda_multiplication_ms'] = cuda_mult_time
    
    return results
