"""
oneDNN backend for the Neurenix framework.

This module provides hardware acceleration using Intel's oneDNN (Deep Neural Network Library),
enabling high-performance deep learning operations on Intel CPUs and GPUs.
"""

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

_onednn_available = False
_onednn_lib = None

try:
    if os.name == 'nt':  # Windows
        try:
            _onednn_lib = ctypes.CDLL('dnnl.dll')
            _onednn_available = True
        except OSError:
            pass
    elif os.name == 'posix':  # Linux/macOS
        try:
            _onednn_lib = ctypes.CDLL('libdnnl.so.2')
            _onednn_available = True
        except OSError:
            try:
                _onednn_lib = ctypes.CDLL('libdnnl.so')
                _onednn_available = True
            except OSError:
                try:
                    _onednn_lib = ctypes.CDLL('libdnnl.1.dylib')  # macOS
                    _onednn_available = True
                except OSError:
                    pass
except Exception:
    _onednn_available = False

def is_onednn_available() -> bool:
    """
    Check if oneDNN is available on the system.
    
    Returns:
        bool: True if oneDNN is available, False otherwise
    """
    return _onednn_available

class OneDNNBackend:
    """
    oneDNN backend for hardware acceleration.
    """
    
    def __init__(self):
        """
        Initialize the oneDNN backend.
        
        Raises:
            RuntimeError: If oneDNN is not available
        """
        if not is_onednn_available():
            raise RuntimeError("oneDNN is not available on this system")
        
        self._engine = None
        self._stream = None
        self._primitives = {}
        self._initialized = False
    
    def initialize(self) -> bool:
        """
        Initialize the oneDNN backend.
        
        Returns:
            bool: True if initialization was successful, False otherwise
        """
        if self._initialized:
            return True
        
        try:
            self._engine = self._create_engine()
            if self._engine is None:
                return False
            
            self._stream = self._create_stream()
            if self._stream is None:
                return False
            
            self._initialized = True
            return True
            
        except Exception as e:
            print(f"oneDNN initialization error: {e}")
            self._cleanup()
            return False
    
    def _cleanup(self):
        """
        Clean up oneDNN resources.
        """
        pass
    
    def _create_engine(self):
        """
        Create oneDNN engine.
        """
        pass
    
    def _create_stream(self):
        """
        Create oneDNN stream.
        """
        pass
    
    def _create_memory_descriptor(self, shape, dtype):
        """
        Create oneDNN memory descriptor.
        """
        pass
    
    def _create_memory(self, memory_desc, data=None):
        """
        Create oneDNN memory.
        """
        pass
    
    def get_device_count(self) -> int:
        """
        Get the number of available oneDNN devices.
        
        Returns:
            int: Number of available devices
        """
        if not self.initialize():
            return 0
        
        return 1
    
    def get_device_info(self, device_index: int = 0) -> Dict[str, Any]:
        """
        Get information about a oneDNN device.
        
        Args:
            device_index: Index of the device
            
        Returns:
            Dict: Device information
        """
        if not self.initialize() or device_index >= 1:
            return {}
        
        return {
            "name": "oneDNN Device",
            "vendor": "Intel",
            "type": "CPU/GPU",
            "compute_units": 0,
            "memory": 0
        }
    
    def matmul(self, a: Tensor, b: Tensor) -> Tensor:
        """
        Perform matrix multiplication using oneDNN.
        
        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 oneDNN.
        
        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 oneDNN.
        
        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)
    
    def rnn(self, input: Tensor, hidden: Tensor, weight_ih: Tensor, weight_hh: Tensor,
           bias_ih: Optional[Tensor] = None, bias_hh: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]:
        """
        Perform RNN operation using oneDNN.
        
        Args:
            input: Input tensor
            hidden: Hidden state tensor
            weight_ih: Input-hidden weights
            weight_hh: Hidden-hidden weights
            bias_ih: Input-hidden bias
            bias_hh: Hidden-hidden bias
            
        Returns:
            Tuple[Tensor, Tensor]: Output and new hidden state
        """
        if not self.initialize():
            from neurenix.nn.functional import rnn
            return rnn(input, hidden, weight_ih, weight_hh, bias_ih, bias_hh)
        
        from neurenix.nn.functional import rnn
        return rnn(input, hidden, weight_ih, weight_hh, bias_ih, bias_hh)
