"""
OpenCL backend for the Neurenix framework.

This module provides hardware acceleration using the OpenCL API,
enabling high-performance computation across different platforms and 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

_opencl_available = False
_opencl_lib = None

try:
    if os.name == 'nt':  # Windows
        _opencl_lib = ctypes.CDLL('OpenCL.dll')
    elif os.name == 'posix':  # Linux/macOS
        try:
            _opencl_lib = ctypes.CDLL('libOpenCL.so.1')
        except OSError:
            try:
                _opencl_lib = ctypes.CDLL('libOpenCL.so')
            except OSError:
                try:
                    _opencl_lib = ctypes.CDLL('libOpenCL.dylib')  # macOS
                except OSError:
                    _opencl_lib = None
    
    if _opencl_lib is not None:
        _opencl_available = True
except Exception:
    _opencl_available = False

def is_opencl_available() -> bool:
    """
    Check if OpenCL is available on the system.
    
    Returns:
        bool: True if OpenCL is available, False otherwise
    """
    return _opencl_available

class OpenCLBackend:
    """
    OpenCL backend for hardware acceleration.
    """
    
    def __init__(self):
        """
        Initialize the OpenCL backend.
        
        Raises:
            RuntimeError: If OpenCL is not available
        """
        if not is_opencl_available():
            raise RuntimeError("OpenCL is not available on this system")
        
        self._platforms = []
        self._devices = []
        self._contexts = []
        self._queues = []
        self._programs = {}
        self._initialized = False
    
    def initialize(self) -> bool:
        """
        Initialize the OpenCL backend.
        
        Returns:
            bool: True if initialization was successful, False otherwise
        """
        if self._initialized:
            return True
        
        try:
            self._platforms = self._get_platforms()
            if not self._platforms:
                return False
            
            for platform in self._platforms:
                platform_devices = self._get_devices(platform)
                if platform_devices:
                    self._devices.extend(platform_devices)
            
            if not self._devices:
                return False
            
            for device in self._devices:
                context = self._create_context([device])
                if context is not None:
                    self._contexts.append(context)
                    queue = self._create_command_queue(context, device)
                    if queue is not None:
                        self._queues.append(queue)
            
            if not self._contexts or not self._queues:
                return False
            
            self._initialized = True
            return True
            
        except Exception as e:
            print(f"OpenCL initialization error: {e}")
            self._cleanup()
            return False
    
    def _cleanup(self):
        """
        Clean up OpenCL resources.
        """
        pass
    
    def _get_platforms(self):
        """
        Get available OpenCL platforms.
        """
        pass
    
    def _get_devices(self, platform):
        """
        Get available OpenCL devices for a platform.
        """
        pass
    
    def _create_context(self, devices):
        """
        Create an OpenCL context for the given devices.
        """
        pass
    
    def _create_command_queue(self, context, device):
        """
        Create an OpenCL command queue for the given context and device.
        """
        pass
    
    def _build_program(self, context, source, options=""):
        """
        Build an OpenCL program from source.
        """
        pass
    
    def get_device_count(self) -> int:
        """
        Get the number of available OpenCL 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 an OpenCL 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": "OpenCL Device",
            "vendor": "Unknown",
            "type": "GPU/CPU",
            "compute_units": 0,
            "memory": 0
        }
    
    def matmul(self, a: Tensor, b: Tensor) -> Tensor:
        """
        Perform matrix multiplication using OpenCL.
        
        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 OpenCL.
        
        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 OpenCL.
        
        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)
