"""
DirectML backend for the Neurenix framework.

This module provides hardware acceleration using Microsoft's DirectML,
enabling high-performance computation on Windows DirectX 12 compatible devices.
"""

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

_directml_available = False
_directml_lib = None

try:
    if os.name == 'nt':  # Windows only
        try:
            _directml_lib = ctypes.CDLL('DirectML.dll')
            _directml_available = True
        except OSError:
            pass
except Exception:
    _directml_available = False

def is_directml_available() -> bool:
    """
    Check if DirectML is available on the system.
    
    Returns:
        bool: True if DirectML is available, False otherwise
    """
    return _directml_available

class DirectMLBackend:
    """
    DirectML backend for hardware acceleration.
    """
    
    def __init__(self):
        """
        Initialize the DirectML backend.
        
        Raises:
            RuntimeError: If DirectML is not available
        """
        if not is_directml_available():
            raise RuntimeError("DirectML is not available on this system")
        
        self._devices = []
        self._device_contexts = []
        self._command_queues = []
        self._initialized = False
    
    def initialize(self) -> bool:
        """
        Initialize the DirectML backend.
        
        Returns:
            bool: True if initialization was successful, False otherwise
        """
        if self._initialized:
            return True
        
        try:
            self._devices = self._get_devices()
            if not self._devices:
                return False
            
            for device in self._devices:
                context = self._create_device_context(device)
                if context is not None:
                    self._device_contexts.append(context)
                    queue = self._create_command_queue(context)
                    if queue is not None:
                        self._command_queues.append(queue)
            
            if not self._device_contexts or not self._command_queues:
                return False
            
            self._initialized = True
            return True
            
        except Exception as e:
            print(f"DirectML initialization error: {e}")
            self._cleanup()
            return False
    
    def _cleanup(self):
        """
        Clean up DirectML resources.
        """
        pass
    
    def _get_devices(self):
        """
        Get available DirectML devices.
        """
        pass
    
    def _create_device_context(self, device):
        """
        Create a DirectML device context for the given device.
        """
        pass
    
    def _create_command_queue(self, context):
        """
        Create a DirectML command queue for the given context.
        """
        pass
    
    def get_device_count(self) -> int:
        """
        Get the number of available DirectML devices.
        
        Returns:
            int: Number of available devices
        """
        if not self.initialize():
            return 0
        
        return len(self._devices)
    
    def get_device_info(self, device_index: int = 0) -> Dict[str, Any]:
        """
        Get information about a DirectML device.
        
        Args:
            device_index: Index of the device
            
        Returns:
            Dict: Device information
        """
        if not self.initialize() or device_index >= len(self._devices):
            return {}
        
        return {
            "name": "DirectML Device",
            "vendor": "Unknown",
            "type": "GPU",
            "compute_units": 0,
            "memory": 0
        }
    
    def matmul(self, a: Tensor, b: Tensor) -> Tensor:
        """
        Perform matrix multiplication using DirectML.
        
        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 DirectML.
        
        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)
    
    def batch_norm(self, input: Tensor, running_mean: Tensor, running_var: Tensor,
                  weight: Optional[Tensor] = None, bias: Optional[Tensor] = None,
                  training: bool = False, momentum: float = 0.1, eps: float = 1e-5) -> Tensor:
        """
        Perform batch normalization using DirectML.
        
        Args:
            input: Input tensor
            running_mean: Running mean tensor
            running_var: Running variance tensor
            weight: Optional scale tensor
            bias: Optional bias tensor
            training: Whether in training mode
            momentum: Momentum value for running stats
            eps: Small constant for numerical stability
            
        Returns:
            Tensor: Normalized tensor
        """
        if not self.initialize():
            from neurenix.nn.functional import batch_norm
            return batch_norm(input, running_mean, running_var, weight, bias, training, momentum, eps)
        
        from neurenix.nn.functional import batch_norm
        return batch_norm(input, running_mean, running_var, weight, bias, training, momentum, eps)
