"""
Helium: Hardware-accelerated deep learning framework
Core framework implementation using virtual GPU infrastructure
"""
import os
import array
from typing import Dict, List, Optional, Union, Any, Tuple

# Virtual GPU driver imports
from virtual_gpu_driver.src.driver_api import VirtualGPUDriver
from virtual_gpu_driver.src.ai.tensor_types import TensorDescriptor, DType, Device, Layout, Tensor

# Helium components
from .multihead_attention import multihead_attention as HeliumMultiHeadAttention
from .layer_norm import layer_norm as HeliumLayerNorm
from .gelu import gelu

# Operation imports
from .normalization import normalize
from .softmax import softmax

# Global device registry
_device_registry = {}
_current_device = None

def register_device(device_id: str, driver: VirtualGPUDriver) -> None:
    """Register a virtual GPU device with Helium"""
    global _device_registry
    _device_registry[device_id] = driver

def get_device(device_id: str) -> VirtualGPUDriver:
    """Get a registered virtual GPU device"""
    if device_id not in _device_registry:
        raise ValueError(f"Device {device_id} not registered. Call register_device first.")
    return _device_registry[device_id]

def set_default_device(device_id: str) -> Dict[str, Any]:
    """Set the default device for new tensors and modules
    
    Returns the hardware capabilities of the selected device
    """
    global _current_device
    if device_id not in _device_registry:
        raise ValueError(f"Device {device_id} not registered. Call register_device first.")
    _current_device = device_id
    
    # Get and display capabilities of newly selected device
    capabilities = get_device_capabilities(device_id)
    print(f"\nSelected virtual GPU {device_id}:")
    print(f"• Total compute: {capabilities['gpus']['total_chips']} chips, {capabilities['compute']['total_sms']} SMs")
    print(f"• Total memory: {capabilities['memory']['total_vram'] / (1024*1024*1024):.1f} GB VRAM")
    print(f"• Max parallelism: {capabilities['compute']['total_sms'] * capabilities['compute']['warps_per_sm'] * capabilities['compute']['threads_per_warp']:,} threads")
    
    return capabilities

def get_default_device() -> VirtualGPUDriver:
    """Get the current default device"""
    global _current_device, _device_registry
    if _current_device is None:
        raise RuntimeError("No default device set. Call set_default_device first.")
    return _device_registry[_current_device]

def get_device_capabilities(device_id: Optional[str] = None) -> Dict[str, Any]:
    """Get hardware capabilities of a virtual GPU device
    
    Returns a dictionary containing:
    - Number of GPUs
    - Number of chips per GPU
    - Number of SMs per chip
    - Number of warps per SM
    - Threads per warp
    - Memory configuration
    - Clock speeds
    """
    driver = get_device(device_id) if device_id else get_default_device()
    
    # Get system configuration
    gpu_system = driver.multi_gpu_system
    
    capabilities = {
        "gpus": {
            "count": len(gpu_system.gpus),
            "chips_per_gpu": len(gpu_system.gpus[0].chips),
            "total_chips": sum(len(gpu.chips) for gpu in gpu_system.gpus)
        },
        "compute": {
            "sms_per_chip": len(gpu_system.gpus[0].chips[0].streaming_multiprocessors),
            "warps_per_sm": gpu_system.gpus[0].chips[0].streaming_multiprocessors[0].max_warps,
            "threads_per_warp": gpu_system.gpus[0].chips[0].streaming_multiprocessors[0].threads_per_warp,
            "total_sms": sum(len(chip.streaming_multiprocessors) 
                            for gpu in gpu_system.gpus 
                            for chip in gpu.chips)
        },
        "memory": {
            "vram_per_gpu": gpu_system.gpus[0].total_memory,
            "total_vram": sum(gpu.total_memory for gpu in gpu_system.gpus),
            "memory_clock": gpu_system.gpus[0].memory_clock
        },
        "clocks": {
            "gpu_clock": gpu_system.gpus[0].core_clock,
            "sm_clock": gpu_system.gpus[0].chips[0].streaming_multiprocessors[0].clock_rate
        }
    }
    
    return capabilities

def init_virtual_gpu(device_id: str = "vgpu0") -> Dict[str, Any]:
    """Initialize and register a new virtual GPU device
    
    Returns the hardware capabilities of the initialized device
    """
    driver = VirtualGPUDriver()
    register_device(device_id, driver)
    set_default_device(device_id)
    
    # Get and display hardware capabilities
    capabilities = get_device_capabilities(device_id)
    
    print(f"\nVirtual GPU {device_id} initialized with:")
    print(f"• {capabilities['gpus']['count']} GPU(s) with {capabilities['gpus']['chips_per_gpu']} chips each")
    print(f"• {capabilities['compute']['sms_per_chip']} SMs per chip")
    print(f"• {capabilities['compute']['warps_per_sm']} warps per SM")
    print(f"• {capabilities['compute']['threads_per_warp']} threads per warp")
    print(f"• {capabilities['memory']['vram_per_gpu'] / (1024*1024*1024):.1f} GB VRAM per GPU")
    print(f"• {capabilities['clocks']['gpu_clock'] / 1e6:.1f} GHz GPU clock")
    print(f"• {capabilities['clocks']['sm_clock'] / 1e6:.1f} GHz SM clock")
    
    return capabilities

class HeliumMLP:
    def __init__(self, config, driver=None, chip_id=0):
        self.config = config
        self.driver = driver
        self.chip_id = chip_id
        
    def __call__(self, x, stream=None, sm_id=0):
        # Implement MLP using driver operations
        return x  # Placeholder until full implementation

class HeliumTensor:
    """Hardware-accelerated tensor using virtual GPU driver"""
    def __init__(self, data=None, shape=None, dtype='float32', device_id=None):
        # Get virtual GPU driver from registry
        self.driver = get_device(device_id) if device_id else get_default_device()
        
        # Set up tensor properties
        self.shape = shape or self._infer_shape(data)
        self.dtype = getattr(DType, dtype.upper()) if isinstance(dtype, str) else dtype
        
        # Create tensor descriptor
        self.descriptor = TensorDescriptor(
            shape=self.shape,
            dtype=self.dtype,
            layout=Layout.ROW_MAJOR,
            device=Device.VGPU
        )
        
        # Create GPU tensor
        self.tensor = Tensor(
            self.driver,
            self.shape,
            self.dtype,
            Device.VGPU,
            Layout.ROW_MAJOR
        )
        
        # Initialize with data if provided
        if data is not None:
            self._initialize_data(data)
            
    def _initialize_data(self, data):
        if isinstance(data, (list, array.array)):
            # Convert to bytes and write directly to GPU memory
            data_bytes = array.array(self._get_typecode(self.dtype.name), data).tobytes()
            self.driver.write_memory(self.tensor.storage.storage.data_ptr, data_bytes, 
                                   self.tensor.storage.storage.chip_id)
                                   
    def _infer_shape(self, data):
        if data is None:
            return (0,)
        if isinstance(data, list):
            shape = [len(data)]
            if data and isinstance(data[0], list):
                shape.extend(self._infer_shape(data[0]))
            return tuple(shape)
        return (1,)
        
    def _calculate_size(self, shape, dtype):
        total_elements = 1
        for dim in shape:
            total_elements *= dim
        return total_elements * self._get_dtype_size(dtype)
        
    def _get_dtype_size(self, dtype):
        sizes = {
            'float32': 4,
            'float64': 8,
            'int32': 4,
            'int64': 8
        }
        return sizes.get(dtype, 4)
        
    def _get_typecode(self, dtype):
        typecodes = {
            'float32': 'f',
            'float64': 'd',
            'int32': 'l',
            'int64': 'q'
        }
        return typecodes.get(dtype, 'f')

class HeliumModule:
    """Base class for hardware-accelerated neural network modules"""
    def __init__(self, device_id=None):
        # Get virtual GPU driver from registry
        self.driver = get_device(device_id) if device_id else get_default_device()
        self.device_id = device_id or _current_device
        self.parameters = {}
        self.training = True
        
    def register_parameter(self, name: str, tensor: HeliumTensor):
        """Register a parameter tensor with the module"""
        self.parameters[name] = tensor
        
    def to(self, device_id: str):
        """Move module to specified virtual GPU device"""
        if device_id not in _device_registry:
            raise ValueError(f"Device {device_id} not registered. Call register_device first.")
        self.driver = get_device(device_id)
        self.device_id = device_id
        # Move all parameters to new device
        for param in self.parameters.values():
            param.driver = self.driver
        return self
        
    def parameters(self):
        """Return an iterator over module parameters"""
        return self.parameters.items()
        
    def get_device(self):
        """Get the device this module is on"""
        return self.device_id
        self.storage.store_state("module_state", self.module_id, state)
        
    def load_state(self, state_id: str):
        """Load module state from remote storage"""
        state = self.storage.load_state("module_state", state_id)
        if state:
            self.module_id = state["module_id"]
            for name, param_state in state["parameters"].items():
                if name in self.parameters:
                    tensor = self.parameters[name]
                    # Transfer tensor data to new location if needed
                    if (tensor.tensor.storage.storage.chip_id != param_state["chip_id"] or
                        tensor.tensor.storage.storage.data_ptr != param_state["addr"]):
                        self.driver.transfer_memory(
                            param_state["addr"],
                            tensor.tensor.storage.storage.data_ptr,
                            param_state["chip_id"],
                            tensor.tensor.storage.storage.chip_id
                        )

class HeliumTransformerBlock(HeliumModule):
    """Hardware-accelerated transformer block"""
    def __init__(self, config):
        super().__init__()
        self.config = config
        
        # Get optimal chip and SM for this block
        self.chip_id = self._get_optimal_chip()
        self.sm_id = 0  # Will be assigned dynamically
        
        # Create sub-modules with hardware acceleration
        self.attention = HeliumMultiHeadAttention(
            config, 
            driver=self.driver,
            chip_id=self.chip_id
        )
        
        self.mlp = HeliumMLP(
            config,
            driver=self.driver,
            chip_id=self.chip_id
        )
        
        self.ln_1 = HeliumLayerNorm(
            config.hidden_size,
            driver=self.driver,
            chip_id=self.chip_id
        )
        
        self.ln_2 = HeliumLayerNorm(
            config.hidden_size,
            driver=self.driver,
            chip_id=self.chip_id
        )
        
        # Initialize stream for operation scheduling
        self.stream = self.driver.create_stream()
        
    def forward(self, hidden_states, attention_mask=None):
        # Ensure inputs are HeliumTensors
        if not isinstance(hidden_states, HeliumTensor):
            hidden_states = HeliumTensor(hidden_states)
            
        # Get optimal SM for this forward pass
        self.sm_id = self._get_optimal_sm()
        
        # Layer norm 1
        norm_states = self.ln_1(hidden_states)
        
        # Self-attention with hardware acceleration
        attn_output = self.attention(
            norm_states, 
            attention_mask,
            stream=self.stream,
            sm_id=self.sm_id
        )
        
        # Residual connection using GPU operation
        self.driver.add_tensor(
            hidden_states.tensor.storage.storage.data_ptr,
            attn_output.tensor.storage.storage.data_ptr,
            hidden_states.tensor.storage.storage.data_ptr,
            self.chip_id,
            self.sm_id,
            self.stream
        )
        
        # Layer norm 2
        norm_states = self.ln_2(hidden_states)
        
        # MLP with hardware acceleration
        mlp_output = self.mlp(
            norm_states,
            stream=self.stream,
            sm_id=self.sm_id
        )
        
        # Final residual connection
        self.driver.add_tensor(
            hidden_states.tensor.storage.storage.data_ptr,
            mlp_output.tensor.storage.storage.data_ptr,
            hidden_states.tensor.storage.storage.data_ptr,
            self.chip_id,
            self.sm_id,
            self.stream
        )
        
        return hidden_states
        
    def _get_optimal_chip(self) -> int:
        """Get optimal GPU chip based on memory availability"""
        gpu_states = self.driver.multi_gpu_system.system_state["global_memory_state"]["allocation_map"]
        available_memory = {
            gpu: state.get("free_memory", float('inf')) 
            for gpu, state in gpu_states.items()
        }
        return max(available_memory.items(), key=lambda x: x[1])[0]
        
    def _get_optimal_sm(self) -> int:
        """Get optimal SM based on current workload"""
        return self.driver.get_optimal_sm(self.chip_id)

class HeliumGPT(HeliumModule):
    """OpenAI GPT model implementation using Helium"""
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.transformer_blocks = [
            HeliumTransformerBlock(config)
            for _ in range(config.num_hidden_layers)
        ]
        self.ln_f = HeliumLayerNorm(config.hidden_size)
        
    def forward(self, input_ids, attention_mask=None):
        hidden_states = self.get_embeddings(input_ids)
        
        for block in self.transformer_blocks:
            hidden_states = block(hidden_states, attention_mask)
            
        hidden_states = self.ln_f(hidden_states)
        logits = self.get_logits(hidden_states)
        
        return logits
        
    def get_embeddings(self, input_ids):
        # Convert input_ids to embeddings using embedding table
        pass
        
    def get_logits(self, hidden_states):
        # Convert final hidden states to logits
        pass

if __name__ == "__main__":
    # Example of creating a tensor
    tensor = HeliumTensor(shape=(3, 3), dtype='float32')
    print(f"Created tensor with shape: {tensor.shape}")
