"""CuPy CUDA compatibility layer for OmniGPU.

This module provides transparent CUDA-to-Metal translation for CuPy operations,
enabling scientific computing with CuPy on Apple Silicon and other non-NVIDIA GPUs.
"""

import logging
import numpy as np
from typing import Any, Optional, Union, Tuple
import functools

logger = logging.getLogger(__name__)

# Track if CuPy is available and patched
_cupy_available = False
_cupy_patched = False
_original_functions = {}

try:
    import cupy
    _cupy_available = True
except ImportError:
    logger.info("CuPy not installed. CuPy patching will be skipped.")
    

def patch():
    """Patch CuPy to use OmniGPU's device management."""
    global _cupy_patched
    
    if not _cupy_available:
        logger.warning("CuPy not available. Skipping CuPy patching.")
        return False
        
    if _cupy_patched:
        logger.debug("CuPy already patched")
        return True
    
    try:
        _patch_cupy_device_management()
        _patch_cupy_memory_operations()
        _patch_cupy_array_creation()
        _patch_cupy_cuda_module()
        _cupy_patched = True
        logger.info("Successfully patched CuPy for OmniGPU")
        return True
    except Exception as e:
        logger.error(f"Failed to patch CuPy: {e}")
        return False


def _patch_cupy_device_management():
    """Patch CuPy's device management to use OmniGPU."""
    import cupy
    from . import device_manager
    
    # Store originals
    _original_functions['cuda.Device'] = cupy.cuda.Device
    _original_functions['cuda.runtime.getDevice'] = cupy.cuda.runtime.getDevice
    _original_functions['cuda.runtime.setDevice'] = cupy.cuda.runtime.setDevice
    
    # Patch device selection
    class UniversalDevice:
        """OmniGPU device wrapper for CuPy."""
        def __init__(self, device_id=None):
            self.device = device_manager.get_device()
            self.id = 0  # Always report device 0 for compatibility
            
        def __enter__(self):
            return self
            
        def __exit__(self, *args):
            pass
            
        def use(self):
            """Make this device current."""
            pass
            
        @property
        def compute_capability(self):
            """Return a fake compute capability for compatibility."""
            return (7, 5)  # Report as SM 7.5
            
    # Replace CuPy's Device class
    cupy.cuda.Device = UniversalDevice
    
    # Patch runtime functions
    def getDevice():
        return 0  # Always return device 0
        
    def setDevice(device_id):
        # Silently accept any device ID
        pass
        
    cupy.cuda.runtime.getDevice = getDevice
    cupy.cuda.runtime.setDevice = setDevice
    
    # Patch device count
    _original_functions['cuda.runtime.getDeviceCount'] = cupy.cuda.runtime.getDeviceCount
    cupy.cuda.runtime.getDeviceCount = lambda: 1  # Always report 1 device


def _patch_cupy_memory_operations():
    """Patch CuPy's memory operations for OmniGPU."""
    import cupy
    from . import device_manager
    
    # Store originals
    _original_functions['cuda.MemoryPool'] = cupy.cuda.MemoryPool
    _original_functions['cuda.runtime.memGetInfo'] = cupy.cuda.runtime.memGetInfo
    
    # Create OmniGPU memory pool
    class UniversalMemoryPool:
        """OmniGPU memory pool for CuPy."""
        def __init__(self):
            self.device = device_manager.get_device()
            
        def malloc(self, size):
            """Allocate memory using PyTorch backend."""
            import torch
            # Use PyTorch for actual allocation
            if str(self.device) == 'cpu':
                # For CPU, return a simple memory view
                return memoryview(bytearray(size))
            else:
                # For GPU, we'll handle this in array creation
                return size  # Return size as placeholder
                
        def free(self, mem):
            """Free memory (no-op for OmniGPU)."""
            pass
            
        def free_all_blocks(self):
            """Free all memory blocks."""
            if hasattr(torch, 'cuda') and hasattr(torch.cuda, 'empty_cache'):
                torch.cuda.empty_cache()
                
    # Replace CuPy's MemoryPool
    cupy.cuda.MemoryPool = UniversalMemoryPool
    
    # Patch memory info
    def memGetInfo():
        """Return fake memory info for compatibility."""
        import torch
        if torch.cuda.is_available():
            # Try to get real info from PyTorch
            try:
                free = torch.cuda.mem_get_info()[0]
                total = torch.cuda.mem_get_info()[1]
                return (free, total)
            except:
                pass
        # Return large fake values
        return (8 * 1024**3, 16 * 1024**3)  # 8GB free, 16GB total
        
    cupy.cuda.runtime.memGetInfo = memGetInfo


def _patch_cupy_array_creation():
    """Patch CuPy array creation to use PyTorch tensors internally."""
    import cupy
    from . import device_manager
    
    # Store original ndarray
    _original_functions['ndarray'] = cupy.ndarray
    _original_functions['asarray'] = cupy.asarray
    _original_functions['array'] = cupy.array
    _original_functions['zeros'] = cupy.zeros
    _original_functions['ones'] = cupy.ones
    _original_functions['empty'] = cupy.empty
    _original_functions['full'] = cupy.full
    
    # Create OmniGPU ndarray wrapper
    class UniversalNdarray(cupy.ndarray):
        """CuPy ndarray backed by PyTorch tensors."""
        
        def __new__(cls, shape, dtype=np.float32, memptr=None, strides=None, order='C'):
            import torch
            
            # Convert numpy dtype to torch dtype
            torch_dtype = _numpy_to_torch_dtype(dtype)
            device = device_manager.get_device()
            
            # Create PyTorch tensor
            if memptr is None:
                # New array
                tensor = torch.empty(shape, dtype=torch_dtype, device=device)
            else:
                # From existing memory (placeholder for now)
                tensor = torch.empty(shape, dtype=torch_dtype, device=device)
                
            # Create CuPy ndarray view
            obj = np.asarray(tensor.cpu().numpy()).view(cls)
            obj._tensor = tensor  # Store PyTorch tensor
            obj._universal_device = device
            
            return obj
            
        @property
        def data(self):
            """Return data pointer (fake for compatibility)."""
            return id(self._tensor)
            
        @property
        def device(self):
            """Return device info."""
            return self._universal_device
            
        def get(self):
            """Transfer array to host."""
            return self._tensor.cpu().numpy()
            
        def set(self, arr):
            """Set array from host data."""
            import torch
            self._tensor.copy_(torch.from_numpy(arr).to(self._universal_device))
            
        def copy(self):
            """Create a copy of the array."""
            new_arr = UniversalNdarray(self.shape, self.dtype)
            new_arr._tensor = self._tensor.clone()
            return new_arr
            
    # Replace CuPy's ndarray
    cupy.ndarray = UniversalNdarray
    
    # Patch array creation functions
    def universal_asarray(a, dtype=None, order='C'):
        """Convert input to OmniGPU array."""
        import torch
        
        if isinstance(a, UniversalNdarray):
            return a
            
        # Convert to numpy first
        if hasattr(a, '__array__'):
            a = np.asarray(a)
            
        # Create OmniGPU array
        arr = UniversalNdarray(a.shape, dtype=dtype or a.dtype)
        arr.set(a)
        return arr
        
    def universal_array(obj, dtype=None, copy=True, order='K', subok=False, ndmin=0):
        """Create OmniGPU array."""
        return universal_asarray(obj, dtype=dtype, order=order)
        
    def universal_zeros(shape, dtype=float, order='C'):
        """Create zero-filled OmniGPU array."""
        arr = UniversalNdarray(shape, dtype=dtype)
        arr._tensor.zero_()
        return arr
        
    def universal_ones(shape, dtype=float, order='C'):
        """Create one-filled OmniGPU array."""
        arr = UniversalNdarray(shape, dtype=dtype)
        arr._tensor.fill_(1)
        return arr
        
    def universal_empty(shape, dtype=float, order='C'):
        """Create uninitialized OmniGPU array."""
        return UniversalNdarray(shape, dtype=dtype)
        
    def universal_full(shape, fill_value, dtype=None, order='C'):
        """Create OmniGPU array filled with value."""
        arr = UniversalNdarray(shape, dtype=dtype)
        arr._tensor.fill_(fill_value)
        return arr
        
    # Apply patches
    cupy.asarray = universal_asarray
    cupy.array = universal_array
    cupy.zeros = universal_zeros
    cupy.ones = universal_ones
    cupy.empty = universal_empty
    cupy.full = universal_full


def _patch_cupy_cuda_module():
    """Patch CuPy's CUDA module functions."""
    import cupy
    
    # Patch CUDA availability check
    _original_functions['cuda.is_available'] = cupy.cuda.is_available
    cupy.cuda.is_available = lambda: True  # Always available with OmniGPU
    
    # Patch synchronize
    _original_functions['cuda.runtime.deviceSynchronize'] = cupy.cuda.runtime.deviceSynchronize
    
    def universal_synchronize():
        """Synchronize with PyTorch backend."""
        import torch
        if hasattr(torch.cuda, 'synchronize'):
            torch.cuda.synchronize()
            
    cupy.cuda.runtime.deviceSynchronize = universal_synchronize
    cupy.cuda.Device.synchronize = universal_synchronize


def _numpy_to_torch_dtype(np_dtype):
    """Convert numpy dtype to torch dtype."""
    import torch
    
    dtype_map = {
        np.float32: torch.float32,
        np.float64: torch.float64,
        np.float16: torch.float16,
        np.int32: torch.int32,
        np.int64: torch.int64,
        np.int16: torch.int16,
        np.int8: torch.int8,
        np.uint8: torch.uint8,
        np.bool_: torch.bool,
    }
    
    if np_dtype in dtype_map:
        return dtype_map[np_dtype]
    else:
        # Default to float32
        logger.warning(f"Unknown numpy dtype {np_dtype}, defaulting to float32")
        return torch.float32


def unpatch():
    """Remove CuPy patches and restore original functions."""
    global _cupy_patched
    
    if not _cupy_available or not _cupy_patched:
        return
        
    try:
        import cupy
        
        # Restore all original functions
        for key, func in _original_functions.items():
            module, attr = key.rsplit('.', 1)
            if module == 'cuda':
                setattr(cupy.cuda, attr, func)
            elif module == 'cuda.runtime':
                setattr(cupy.cuda.runtime, attr, func)
            else:
                setattr(cupy, attr, func)
                
        _original_functions.clear()
        _cupy_patched = False
        logger.info("Successfully unpatched CuPy")
        
    except Exception as e:
        logger.error(f"Failed to unpatch CuPy: {e}")