"""
Enhanced Hardware Abstraction Layer (HAL) for Virtual GPU
Integrates with SQLite for local state management and multi-GPU support
"""

import json
import time
from typing import Dict, Any, Optional, List, Tuple
from enum import Enum
import numpy as np

class HardwareType(Enum):
    COMPUTE_UNIT = "compute_unit"
    TENSOR_CORE = "tensor_core"
    SHADER_UNIT = "shader_unit"
    MEMORY_CONTROLLER = "memory_controller"
    DMA_ENGINE = "dma_engine"
    OPTICAL_INTERCONNECT = "optical_interconnect"

class HardwareAbstractionLayer:
    def __init__(self, storage_manager):
        """Initialize HAL with storage manager"""
        self.storage_manager = storage_manager
        self._setup_database()

    def _setup_database(self):
        """Initialize hardware state in storage"""
        # Initialize basic hardware components
        components = {
            "gpu_chip": {
                "sm_count": 8,
                "clock_speed_mhz": 1500,
                "memory_size_gb": 8,
                "state": "ready"
            },
            "streaming_multiprocessors": {
                "count": 8,
                "cores_per_sm": 128,
                "tensor_cores": 4,
                "state": "ready"
            },
            "memory_controller": {
                "total_size": 8 * 1024 * 1024 * 1024,  # 8GB
                "allocated": 0,
                "state": "ready"
            },
            "shader_units": {
                "count": 1024,
                "active": 0,
                "state": "ready"
            }
        }
        
        # Store initial state for each component
        for hw_type, state in components.items():
            self.storage_manager.store_state(
                hardware_id=f"{hw_type}_0",
                hardware_type=hw_type,
                state=state
            )
    def configure_hardware(self, hardware_type: HardwareType, config: Dict[str, Any]):
        """Configure hardware component with given settings"""
        hw_id = f"{hardware_type.value}_0"
        self.storage_manager.store_state(hw_id, hardware_type.value, config)

    def get_hardware_info(self, hardware_type: HardwareType) -> Optional[Dict[str, Any]]:
        """Get current configuration and state of hardware component"""
        hw_id = f"{hardware_type.value}_0"
        state = self.storage_manager.get_state(hw_id, hardware_type.value)
        return state if state else None

    def get_frame_buffer(self) -> Optional[bytes]:
        """Get current frame buffer content"""
        state = self.storage_manager.get_state("frame_buffer_0", "frame_buffer")
        if state and 'data' in state:
            return state['data']
        return None
    def update_frame_buffer(self, data: bytes):
        """Update frame buffer with new content"""
        self.storage_manager.store_state(
            "frame_buffer_0",
            "frame_buffer",
            {"data": data, "timestamp": time.time()}
        )

    def execute_compute(self, data: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """Execute compute operation on available compute unit"""
        compute_unit = self.get_hardware_info(HardwareType.COMPUTE_UNIT)
        if not compute_unit or compute_unit.get('state') != 'ready':
            return None
        
        # Initialize SMs for the chip
        sm_data = {
            "sm_id": [i for i in range(64)],
            "chip_id": [chip_id] * 64,
            "core_count": [128] * 64,
            "tensor_core_count": [4] * 64,
            "state_json": [json.dumps({"active": False})] * 64
        }
        self.storage_manager.update_table("streaming_multiprocessors", sm_data)
        
        # Initialize shader units
        shader_data = {
            "unit_id": [i for j in range(64) for i in range(16)],
            "chip_id": [chip_id] * (64 * 16),
            "sm_id": [j for j in range(64) for i in range(16)],
            "current_program_id": [None] * (64 * 16),
            "state_json": [json.dumps({"active": False})] * (64 * 16)
        }
        self.storage_manager.update_table("shader_units", shader_data)
        
        self.storage_manager.save_dataset()
        
        # Recursively call to retrieve the newly created chip
        return self.get_chip(chip_id)

    def connect_chips(self, chip_id_a: int, chip_id_b: int, bandwidth_tbps: float = 800, latency_ns: float = 1):
        """Connect two chips with an optical interconnect"""
        link_id = f"link_{chip_id_a}_{chip_id_b}"
        
        # Ensure both chips exist
        self.get_chip(chip_id_a)
        self.get_chip(chip_id_b)
        
        # Create interconnect
        self.storage_manager.update_table("optical_interconnects", {
            "link_id": [link_id],
            "chip_a_id": [chip_id_a],
            "chip_b_id": [chip_id_b],
            "bandwidth_tbps": [bandwidth_tbps],
            "latency_ns": [latency_ns],
            "state_json": [json.dumps({"active": True, "errors": 0})]
        })
        self.storage_manager.save_dataset()

    def configure_hardware(self, hardware_type: HardwareType, config: Dict[str, Any]):
        """Configure hardware component with given settings"""
        hw_id = f"{hardware_type.value}_0"
        self.storage_manager.store_state(hw_id, hardware_type.value, config)

    def get_hardware_info(self, hardware_type: HardwareType) -> Optional[Dict[str, Any]]:
        """Get current configuration and state of hardware component"""
        hw_id = f"{hardware_type.value}_0"
        state = self.storage_manager.get_state(hw_id, hardware_type.value)
        return state if state else None

    def get_frame_buffer(self) -> Optional[bytes]:
        """Get current frame buffer content"""
        state = self.storage_manager.get_state("frame_buffer_0", "frame_buffer")
        if state and 'data' in state:
            return state['data']
        return None

    def update_frame_buffer(self, data: bytes):
        """Update frame buffer with new content"""
        self.storage_manager.store_state(
            "frame_buffer_0",
            "frame_buffer",
            {"data": data, "timestamp": time.time()}
        )

    def execute_compute(self, data: Dict[str, Any]) -> Optional[Dict[str, Any]]:
        """Execute compute operation on available compute unit"""
        compute_unit = self.get_hardware_info(HardwareType.COMPUTE_UNIT)
        if not compute_unit or compute_unit.get('state') != 'ready':
            return None

        try:
            # Mark compute unit as busy
            compute_unit['state'] = 'busy'
            self.configure_hardware(HardwareType.COMPUTE_UNIT, compute_unit)

            # Process computation
            result = {'status': 'success', 'result': data}

            # Mark compute unit as ready
            compute_unit['state'] = 'ready'
            self.configure_hardware(HardwareType.COMPUTE_UNIT, compute_unit)

            return result
        except Exception as e:
            if compute_unit:
                compute_unit['state'] = 'error'
                compute_unit['error'] = str(e)
                self.configure_hardware(HardwareType.COMPUTE_UNIT, compute_unit)
            return {'status': 'error', 'error': str(e)}
    def v2_vertex_shader(self, chip_id: int, vertex_data: List[float], 
                          shader_program: Dict[str, Any]) -> List[float]:
        """
        Run vertex shader using provided instructions.
        Supports AI/ML ops: matmul, activation, softmax, etc.
        """
        chip = self.get_chip(chip_id)
        # Assuming SMs are directly accessible or passed
        # For simplicity, we'll use a dummy SM for now
        class DummySM:
            def tensor_core_matmul(self, A, B): return np.matmul(A, B)
        sm = DummySM()

        registers = list(vertex_data)
        
        for instr in shader_program.get("instructions", []):
            op = instr.get("opcode")
            args = instr.get("args", [])
            
            if op == "load_vertex_data":
                continue
            elif op == "transform_vertex":
                registers = [v * 2 for v in registers]
            elif op == "matmul":
                A = args[0] if args else [[v] for v in registers]
                B = args[1] if len(args) > 1 else [[1.0] * len(registers)]
                result = sm.tensor_core_matmul(np.array(A), np.array(B))
                if result is not None:
                    registers = result.flatten().tolist()
            elif op == "activation":
                registers = [max(0, v) for v in registers]  # ReLU
            elif op == "softmax":
                import math
                exp_vals = [math.exp(v) for v in registers]
                s = sum(exp_vals)
                registers = [v / s for v in exp_vals]
                
        return registers
        
    def v2_fragment_shader(self, chip_id: int, fragment_data: Dict[str, Any], 
                          shader_program: Dict[str, Any]) -> Tuple[float, float, float, float]:
        """
        Run fragment shader using provided instructions.
        Supports AI/ML ops: matmul, activation, softmax, etc.
        """
        chip = self.get_chip(chip_id)
        # Assuming SMs are directly accessible or passed
        # For simplicity, we'll use a dummy SM for now
        class DummySM:
            def tensor_core_matmul(self, A, B): return np.matmul(A, B)
        sm = DummySM()

        color = [1.0, 1.0, 1.0, 1.0]  # Default white
        
        for instr in shader_program.get("instructions", []):
            op = instr.get("opcode")
            args = instr.get("args", [])
            
            if op == "load_fragment_data":
                continue
            elif op == "compute_color":
                x = fragment_data.get("x", 0)
                y = fragment_data.get("y", 0)
                color = [x % 256 / 255.0, y % 256 / 255.0, 0.5, 1.0]
            elif op == "matmul":
                A = args[0] if args else [[c] for c in color]
                B = args[1] if len(args) > 1 else [[1.0] * len(color)]
                result = sm.tensor_core_matmul(np.array(A), np.array(B))
                if result is not None:
                    color = result.flatten().tolist()
            elif op == "activation":
                color = [max(0, v) for v in color]  # ReLU
            elif op == "softmax":
                import math
                exp_vals = [math.exp(v) for v in color]
                s = sum(exp_vals)
                color = [v / s for v in exp_vals]
                
        return tuple(color[:4])  # Ensure RGBA output
        
    def allocate_vram(self, chip_id: int, size_bytes: int) -> Optional[str]:
        """Allocate VRAM on specified chip"""
        # This should ideally interact with HuggingFace storage for VRAM allocation
        # For now, return a dummy ID
        return f"vram_block_{chip_id}_{size_bytes}_{uuid.uuid4()}"
        
    def transfer_data(self, src_chip_id: int, dst_chip_id: int, size_bytes: int) -> float:
        """Transfer data between chips, returns transfer time"""
        # This should ideally interact with HuggingFace storage for data transfer
        # For now, return a dummy transfer time
        return 0.001  # very fast transfer
