"""
oneAPI backend for the Neurenix framework.

This module provides hardware acceleration using Intel's oneAPI,
enabling high-performance computation across Intel CPUs, GPUs, and FPGAs.
"""

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

_oneapi_available = False
_oneapi_lib = None

try:
    if os.name == 'nt':  # Windows
        try:
            _oneapi_lib = ctypes.CDLL('sycl.dll')
            _oneapi_available = True
        except OSError:
            pass
    elif os.name == 'posix':  # Linux/macOS
        try:
            _oneapi_lib = ctypes.CDLL('libsycl.so.5')
            _oneapi_available = True
        except OSError:
            try:
                _oneapi_lib = ctypes.CDLL('libsycl.so')
                _oneapi_available = True
            except OSError:
                pass
except Exception:
    _oneapi_available = False

def is_oneapi_available() -> bool:
    """
    Check if oneAPI is available on the system.
    
    Returns:
        bool: True if oneAPI is available, False otherwise
    """
    return _oneapi_available

class OneAPIBackend:
    """
    oneAPI backend for hardware acceleration.
    """
    
    def __init__(self):
        """
        Initialize the oneAPI backend.
        
        Raises:
            RuntimeError: If oneAPI is not available
        """
        if not is_oneapi_available():
            raise RuntimeError("oneAPI is not available on this system")
        
        self._devices = []
        self._queues = []
        self._initialized = False
    
    def initialize(self) -> bool:
        """
        Initialize the oneAPI 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:
                queue = self._create_queue(device)
                if queue is not None:
                    self._queues.append(queue)
            
            if not self._queues:
                return False
            
            self._initialized = True
            return True
            
        except Exception as e:
            print(f"oneAPI initialization error: {e}")
            self._cleanup()
            return False
    
    def _cleanup(self):
        """
        Clean up oneAPI resources.
        """
        pass
    
    def _get_devices(self):
        """
        Get available oneAPI devices.
        """
        pass
    
    def _create_queue(self, device):
        """
        Create a oneAPI queue for the given device.
        """
        pass
    
    def get_device_count(self) -> int:
        """
        Get the number of available oneAPI 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 oneAPI 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": "oneAPI Device",
            "vendor": "Intel",
            "type": "CPU/GPU/FPGA",
            "compute_units": 0,
            "memory": 0
        }
    
    def matmul(self, a: Tensor, b: Tensor) -> Tensor:
        """
        Perform matrix multiplication using oneAPI.
        
        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 oneAPI.
        
        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 oneAPI.
        
        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)
