from streaming_multiprocessor import StreamingMultiprocessor
from tensor_core import TensorCoreArray
from multicore import MultiCoreSystem
from virtual_vram import VirtualVRAM
from gpu_chip import GPUChip
from http_storage import LocalStorage
from matrix_ops import MatrixOpScheduler

class HardwareManager:
    def __init__(self):
        # Initialize storage system with single local storage instance
        self.storage = LocalStorage(db_path="db/coin_miner/hardware.db")
        
        # Initialize virtual memory
        self.vram = VirtualVRAM(
            size_gb=800,  # 800GB VRAM
            storage=self.storage
        )
        
        # Initialize GPU components
        self.gpu_chip = GPUChip()
        self.tensor_cores = TensorCoreArray(
            num_tensor_cores=8000,
            memory_size=800*1024*1024*1024,  # 800GB
            bandwidth_tbps=10000,
            bits=2
        )
        
        # Initialize streaming multiprocessors
        self.streaming_mps = [
            StreamingMultiprocessor(
                sm_id=i,
                num_cores=128,
                storage=self.storage
            ) for i in range(164)  # 164 SMs
        ]
        
        # Initialize CPU system
        self.cpu_system = MultiCoreSystem(
            num_cores=50000,          # 50k cores
            threads_per_core=700000,  # 700k threads per core
            threads_per_block=32,
            bits=2
        )
        
        # Initialize matrix operation scheduler
        self.matrix_scheduler = MatrixOpScheduler(
            num_sms=164,
            cores_per_sm=128
        )

    def initialize(self):
        """Initialize all hardware components"""
        # Configure virtual memory
        self.vram.initialize_memory_pools()
        
        # Configure GPU components
        self.gpu_chip.initialize_chip()
        self.tensor_cores.initialize_cores()
        
        # Initialize streaming multiprocessors
        for sm in self.streaming_mps:
            sm.initialize()
            
        # Initialize CPU system
        self.cpu_system.initialize_cores()

    def allocate_mining_resources(self, workload_size):
        """Allocate resources for mining operations"""
        # Allocate VRAM for mining data
        vram_allocation = self.vram.allocate_memory_block(
            size=workload_size,
            purpose="mining_data"
        )
        
        # Get available tensor cores
        available_cores = self.tensor_cores.get_available_cores()
        
        # Get available CPU threads
        cpu_resources = self.cpu_system.get_available_threads()
        
        return {
            'vram_allocation': vram_allocation,
            'tensor_cores': available_cores,
            'cpu_threads': cpu_resources
        }

    def schedule_mining_operation(self, operation_data):
        """Schedule a mining operation across available hardware"""
        # Distribute work across streaming multiprocessors
        sm_assignments = self.matrix_scheduler.schedule_operation(
            op_type="mining",
            input_data=operation_data
        )
        
        # Assign tensor cores for parallel processing
        tensor_assignments = self.tensor_cores.allocate_computation_units(
            operation_data
        )
        
        # Schedule CPU threads for auxiliary operations
        cpu_assignments = self.cpu_system.schedule_threads(
            operation_data
        )
        
        return {
            'sm_assignments': sm_assignments,
            'tensor_assignments': tensor_assignments,
            'cpu_assignments': cpu_assignments
        }

    def execute_mining_operation(self, operation_config):
        """Execute a mining operation using allocated resources"""
        try:
            # Distribute work across streaming multiprocessors
            for sm_id, work_unit in operation_config['sm_assignments'].items():
                self.streaming_mps[sm_id].tensor_core_matmul_from_memory(
                    addr_A=work_unit['input_addr'],
                    shape_A=work_unit['input_shape'],
                    addr_B=work_unit['weight_addr'],
                    shape_B=work_unit['weight_shape'],
                    addr_C=work_unit['output_addr'],
                    tensor_core_id=work_unit['tensor_core_id']
                )
            
            # Process on tensor cores
            self.tensor_cores.execute_parallel_operations(
                operation_config['tensor_assignments']
            )
            
            # Execute CPU parallel operations
            self.cpu_system.execute_parallel_work(
                operation_config['cpu_assignments']
            )
            
            return True
        except Exception as e:
            print(f"Mining operation failed: {str(e)}")
            return False

    def monitor_hardware_status(self):
        """Monitor hardware status and performance"""
        return {
            'gpu_temperature': self.gpu_chip.get_temperature(),
            'vram_usage': self.vram.get_memory_usage(),
            'tensor_core_utilization': self.tensor_cores.get_utilization(),
            'sm_status': [sm.get_status() for sm in self.streaming_mps],
            'cpu_utilization': self.cpu_system.get_utilization()
        }

    def cleanup_resources(self):
        """Clean up and release hardware resources"""
        self.vram.cleanup_memory_pools()
        self.tensor_cores.cleanup()
        for sm in self.streaming_mps:
            sm.cleanup()
        self.cpu_system.cleanup()
