"""
MKL-DNN backend for the Neurenix framework.

This module provides hardware acceleration using Intel's MKL-DNN (Math Kernel Library for Deep Neural Networks),
enabling high-performance deep learning operations on Intel CPUs.
"""

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

_mkldnn_available = False
_mkldnn_lib = None

try:
    if os.name == 'nt':  # Windows
        try:
            _mkldnn_lib = ctypes.CDLL('mkldnn.dll')
            _mkldnn_available = True
        except OSError:
            pass
    elif os.name == 'posix':  # Linux/macOS
        try:
            _mkldnn_lib = ctypes.CDLL('libmkldnn.so.1')
            _mkldnn_available = True
        except OSError:
            try:
                _mkldnn_lib = ctypes.CDLL('libmkldnn.so')
                _mkldnn_available = True
            except OSError:
                try:
                    _mkldnn_lib = ctypes.CDLL('libmkldnn.1.dylib')  # macOS
                    _mkldnn_available = True
                except OSError:
                    pass
except Exception:
    _mkldnn_available = False

def is_mkldnn_available() -> bool:
    """
    Check if MKL-DNN is available on the system.
    
    Returns:
        bool: True if MKL-DNN is available, False otherwise
    """
    return _mkldnn_available

class MKLDNNBackend:
    """
    MKL-DNN backend for hardware acceleration.
    """
    
    def __init__(self):
        """
        Initialize the MKL-DNN backend.
        
        Raises:
            RuntimeError: If MKL-DNN is not available
        """
        if not is_mkldnn_available():
            raise RuntimeError("MKL-DNN is not available on this system")
        
        self._engine = None
        self._stream = None
        self._primitives = {}
        self._initialized = False
    
    def initialize(self) -> bool:
        """
        Initialize the MKL-DNN 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"MKL-DNN initialization error: {e}")
            self._cleanup()
            return False
    
    def _cleanup(self):
        """
        Clean up MKL-DNN resources.
        """
        pass
    
    def _create_engine(self):
        """
        Create MKL-DNN engine.
        """
        pass
    
    def _create_stream(self):
        """
        Create MKL-DNN stream.
        """
        pass
    
    def _create_memory_descriptor(self, shape, dtype):
        """
        Create MKL-DNN memory descriptor.
        """
        pass
    
    def _create_memory(self, memory_desc, data=None):
        """
        Create MKL-DNN memory.
        """
        pass
    
    def get_device_count(self) -> int:
        """
        Get the number of available MKL-DNN 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 MKL-DNN device.
        
        Args:
            device_index: Index of the device
            
        Returns:
            Dict: Device information
        """
        if not self.initialize() or device_index >= 1:
            return {}
        
        return {
            "name": "MKL-DNN Device",
            "vendor": "Intel",
            "type": "CPU",
            "compute_units": 0,
            "memory": 0
        }
    
    def matmul(self, a: Tensor, b: Tensor) -> Tensor:
        """
        Perform matrix multiplication using MKL-DNN.
        
        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 MKL-DNN.
        
        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 MKL-DNN.
        
        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 lstm(self, input: Tensor, hidden: Tuple[Tensor, Tensor], weight_ih: Tensor, weight_hh: Tensor,
            bias_ih: Optional[Tensor] = None, bias_hh: Optional[Tensor] = None) -> Tuple[Tensor, Tuple[Tensor, Tensor]]:
        """
        Perform LSTM operation using MKL-DNN.
        
        Args:
            input: Input tensor
            hidden: Hidden state tuple (h, c)
            weight_ih: Input-hidden weights
            weight_hh: Hidden-hidden weights
            bias_ih: Input-hidden bias
            bias_hh: Hidden-hidden bias
            
        Returns:
            Tuple[Tensor, Tuple[Tensor, Tensor]]: Output and new hidden state
        """
        if not self.initialize():
            from neurenix.nn.functional import lstm
            return lstm(input, hidden, weight_ih, weight_hh, bias_ih, bias_hh)
        
        from neurenix.nn.functional import lstm
        return lstm(input, hidden, weight_ih, weight_hh, bias_ih, bias_hh)
