"""
Vulkan backend for the Neurenix framework.

This module provides hardware acceleration using the Vulkan API,
enabling high-performance GPU computation across different platforms.
"""

from typing import Optional, List, Dict, Any, Union, Tuple
import os
import ctypes
import numpy as np

from neurenix.tensor import Tensor
from neurenix.device import Device, DeviceType

_vulkan_available = False
_vulkan_lib = None

try:
    if os.name == 'nt':  # Windows
        _vulkan_lib = ctypes.CDLL('vulkan-1.dll')
    elif os.name == 'posix':  # Linux/macOS
        try:
            _vulkan_lib = ctypes.CDLL('libvulkan.so.1')
        except OSError:
            try:
                _vulkan_lib = ctypes.CDLL('libvulkan.so')
            except OSError:
                try:
                    _vulkan_lib = ctypes.CDLL('libMoltenVK.dylib')  # macOS with MoltenVK
                except OSError:
                    _vulkan_lib = None
    
    if _vulkan_lib is not None:
        _vulkan_available = True
except Exception:
    _vulkan_available = False

def is_vulkan_available() -> bool:
    """
    Check if Vulkan is available on the system.
    
    Returns:
        bool: True if Vulkan is available, False otherwise
    """
    return _vulkan_available

class VulkanBackend:
    """
    Vulkan backend for hardware acceleration.
    """
    
    def __init__(self):
        """
        Initialize the Vulkan backend.
        
        Raises:
            RuntimeError: If Vulkan is not available
        """
        if not is_vulkan_available():
            raise RuntimeError("Vulkan is not available on this system")
        
        self._instance = None
        self._physical_devices = []
        self._logical_devices = []
        self._compute_queues = []
        self._command_pools = []
        self._initialized = False
    
    def initialize(self) -> bool:
        """
        Initialize the Vulkan backend.
        
        Returns:
            bool: True if initialization was successful, False otherwise
        """
        if self._initialized:
            return True
        
        try:
            app_info = self._create_application_info()
            instance_info = self._create_instance_info(app_info)
            self._instance = self._create_instance(instance_info)
            
            self._physical_devices = self._enumerate_physical_devices()
            if not self._physical_devices:
                return False
            
            for physical_device in self._physical_devices:
                logical_device, compute_queue, command_pool = self._create_compute_device(physical_device)
                if logical_device is not None:
                    self._logical_devices.append(logical_device)
                    self._compute_queues.append(compute_queue)
                    self._command_pools.append(command_pool)
            
            if not self._logical_devices:
                return False
            
            self._initialized = True
            return True
            
        except Exception as e:
            print(f"Vulkan initialization error: {e}")
            self._cleanup()
            return False
    
    def _cleanup(self):
        """
        Clean up Vulkan resources.
        """
        pass
    
    def _create_application_info(self):
        """
        Create Vulkan application info structure.
        """
        pass
    
    def _create_instance_info(self, app_info):
        """
        Create Vulkan instance info structure.
        """
        pass
    
    def _create_instance(self, instance_info):
        """
        Create Vulkan instance.
        """
        pass
    
    def _enumerate_physical_devices(self):
        """
        Enumerate physical devices.
        """
        pass
    
    def _create_compute_device(self, physical_device):
        """
        Create logical device for compute operations.
        """
        pass
    
    def get_device_count(self) -> int:
        """
        Get the number of available Vulkan devices.
        
        Returns:
            int: Number of available devices
        """
        if not self.initialize():
            return 0
        
        return len(self._logical_devices)
    
    def get_device_info(self, device_index: int = 0) -> Dict[str, Any]:
        """
        Get information about a Vulkan device.
        
        Args:
            device_index: Index of the device
            
        Returns:
            Dict: Device information
        """
        if not self.initialize() or device_index >= len(self._physical_devices):
            return {}
        
        return {
            "name": "Vulkan Device",
            "vendor": "Unknown",
            "type": "GPU",
            "compute_units": 0,
            "memory": 0
        }
    
    def matmul(self, a: Tensor, b: Tensor) -> Tensor:
        """
        Perform matrix multiplication using Vulkan.
        
        Args:
            a: First tensor
            b: Second tensor
            
        Returns:
            Tensor: Result of matrix multiplication
        """
        if not self.initialize():
            return a @ b
        
        return a @ b
    
    def conv2d(self, input: Tensor, weight: Tensor, bias: Optional[Tensor] = None,
              stride: Tuple[int, int] = (1, 1), padding: Tuple[int, int] = (0, 0),
              dilation: Tuple[int, int] = (1, 1), groups: int = 1) -> Tensor:
        """
        Perform 2D convolution using Vulkan.
        
        Args:
            input: Input tensor
            weight: Weight tensor
            bias: Optional bias tensor
            stride: Convolution stride
            padding: Convolution padding
            dilation: Convolution dilation
            groups: Convolution groups
            
        Returns:
            Tensor: Result of convolution
        """
        if not self.initialize():
            from neurenix.nn.functional import conv2d
            return conv2d(input, weight, bias, stride, padding, dilation, groups)
        
        from neurenix.nn.functional import conv2d
        return conv2d(input, weight, bias, stride, padding, dilation, groups)
