"""
HyperScale Multi-GPU System with Streaming Operations for Extreme Mining Performance
"""
from typing import Dict, Any, List, Optional, Tuple
import numpy as np
import time
import os
import threading
from concurrent.futures import ThreadPoolExecutor
from electron_speed import max_switch_freq, GATE_DELAY
from disk_storage import DiskStorageManager
from gpu_parallel_distributor import GPUParallelDistributor
from gpu_chip import GPUChip, ChipType
from tensor_core import TensorCore
from tensor_storage import TensorStorage
from cross_gpu_stream import CrossGPUStream
from streaming_multiprocessor import StreamingMultiprocessor
from parallel_array_distributor import ParallelArrayDistributor

class OpticalInterconnect:
    """Ultra-fast optical interconnect between GPUs"""
    def __init__(self, bandwidth_tbps: float = 1600, latency_ns: float = 0.5):
        self.bandwidth = bandwidth_tbps * 1e12  # Convert to bps
        self.latency = latency_ns * 1e-9  # Convert to seconds
        self.active_connections = {}
        self.lock = threading.Lock()
        
    def connect(self, source_id: int, target_id: int) -> str:
        """Establish optical connection between GPUs"""
        connection_id = f"{source_id}_{target_id}_{time.time_ns()}"
        with self.lock:
            self.active_connections[connection_id] = {
                'source': source_id,
                'target': target_id,
                'established': time.time(),
                'data_transferred': 0
            }
        return connection_id
        
    def transfer(self, connection_id: str, data_size: int) -> float:
        """Calculate transfer time for data size in bytes"""
        if connection_id not in self.active_connections:
            raise ValueError("Invalid connection ID")
            
        transfer_time = data_size * 8 / self.bandwidth  # Convert bytes to bits
        total_time = transfer_time + self.latency
        
        with self.lock:
            self.active_connections[connection_id]['data_transferred'] += data_size
            
        return total_time

class HyperScaleGPU:
    # System constants based on electron physics
    SWITCH_FREQ = 9.80e14  # Hz (based on flip-flop max frequency)
    GATE_DELAY = 1.02e-15  # seconds
    
    # Massive GPU scaling for 3 BTC/2hr target
    GPUS_PER_UNIT = 1024        # GPUs per computing unit
    UNITS_PER_CLUSTER = 1024    # Computing units per cluster
    TOTAL_CLUSTERS = 1024       # Total clusters
    
    # Network stats (2025)
    NETWORK_HASHRATE = 500e18   # 500 EH/s network hashrate
    BLOCK_TIME = 600           # 10 minutes
    BLOCK_REWARD = 3.125       # BTC per block
    
    # Enhanced GPU specs
    TENSOR_CORES_PER_SM = 2024  # Tensor cores per streaming multiprocessor
    SMS_PER_GPU = 1024         # Streaming multiprocessors per GPU
    STREAM_BLOCK_SIZE = 256    # Stream in 256-byte blocks (SHA-256 optimal)
    
    def __init__(self):
        self.storage = DiskStorageManager("gpu_storage", compression="lzma")
        self.interconnect = OpticalInterconnect(bandwidth_tbps=1600, latency_ns=0.5)
        
        # Initialize GPU clusters
        self.clusters = {}
        self.tensor_cores = {}
        
        # Initialize parallel distributor
        total_gpus = self.GPUS_PER_UNIT * self.UNITS_PER_CLUSTER * self.TOTAL_CLUSTERS
        self.distributor = GPUParallelDistributor(num_gpus=total_gpus)
        
        # Initialize the system
        self._initialize_system()
        
    def _initialize_system(self):
        """Initialize the massive GPU infrastructure"""
        total_gpus = self.GPUS_PER_UNIT * self.UNITS_PER_CLUSTER * self.TOTAL_CLUSTERS
        print(f"Initializing HyperScale GPU System with {total_gpus:,} GPUs")
        
        # Initialize GPU chips and tensor cores
        for cluster_id in range(self.TOTAL_CLUSTERS):
            self.clusters[cluster_id] = {}
            
            for unit_id in range(self.UNITS_PER_CLUSTER):
                self.clusters[cluster_id][unit_id] = {}
                
                for gpu_id in range(self.GPUS_PER_UNIT):
                    # Create GPU chip
                    absolute_gpu_id = (cluster_id * self.UNITS_PER_CLUSTER * self.GPUS_PER_UNIT + 
                                     unit_id * self.GPUS_PER_UNIT + gpu_id)
                                     
                    gpu_chip = GPUChip(
                        chip_id=absolute_gpu_id,
                        chip_type=ChipType.TENSOR,
                        num_cores=self.TENSOR_CORES_PER_SM * self.SMS_PER_GPU,
                        stream_block_size=self.STREAM_BLOCK_SIZE
                    )
                    
                    # Set up streaming storage for this GPU
                    stream_path = f"gpu_storage/gpu_{absolute_gpu_id}"
                    os.makedirs(stream_path, exist_ok=True)
                    
                    # Initialize streaming multiprocessors
                    sms = []
                    for sm_id in range(self.SMS_PER_GPU):
                        sm = StreamingMultiprocessor(
                            sm_id=sm_id,
                            num_tensor_cores=self.TENSOR_CORES_PER_SM,
                            gpu_id=absolute_gpu_id
                        )
                        sms.append(sm)
                    
                    # Initialize tensor cores for this GPU
                    self.tensor_cores[absolute_gpu_id] = []
                    for core_id in range(self.TENSOR_CORES_PER_SM * self.SMS_PER_GPU):
                        tensor_core = TensorCore(
                            core_id=core_id,
                            gpu_id=absolute_gpu_id,
                            switching_freq=self.SWITCH_FREQ
                        )
                        self.tensor_cores[absolute_gpu_id].append(tensor_core)
                    
                    # Add GPU to cluster
                    self.clusters[cluster_id][unit_id][gpu_id] = {
                        'chip': gpu_chip,
                        'sms': sms,
                        'tensor_cores': self.tensor_cores[absolute_gpu_id],
                        'stream': CrossGPUStream(gpu_id=absolute_gpu_id)
                    }
                    
        # Initialize streaming tensor storage
        self.tensor_storage = TensorStorage(
            num_gpus=total_gpus,
            storage_path="gpu_storage",
            stream_block_size=self.STREAM_BLOCK_SIZE,
            use_streaming=True  # Enable pure streaming mode
        )
        
        print(f"System initialized with {total_gpus * self.TENSOR_CORES_PER_SM * self.SMS_PER_GPU:,} tensor cores")
        print(f"Using pure streaming storage with {self.STREAM_BLOCK_SIZE}-byte blocks")
        
    def start_mining(self, block_template: bytes):
        """Start mining operations across all GPUs"""
        total_gpus = self.GPUS_PER_UNIT * self.UNITS_PER_CLUSTER * self.TOTAL_CLUSTERS
        
        # Create parallel work distribution
        work_chunks = self.distributor.distribute_work(
            block_template, 
            num_chunks=total_gpus
        )
        
        # Create tensor arrays for each GPU
        tensor_arrays = []
        for gpu_id in range(total_gpus):
            # Allocate tensor storage
            tensor_array = self.tensor_storage.allocate_array(
                gpu_id=gpu_id,
                shape=(len(work_chunks[gpu_id]), 256),  # 256 bytes per block
                dtype=np.uint8
            )
            tensor_arrays.append(tensor_array)
            
        # Stream data to GPUs in parallel
        futures = []
        with ThreadPoolExecutor(max_workers=total_gpus) as executor:
            for gpu_id in range(total_gpus):
                cluster_id = gpu_id // (self.UNITS_PER_CLUSTER * self.GPUS_PER_UNIT)
                unit_id = (gpu_id % (self.UNITS_PER_CLUSTER * self.GPUS_PER_UNIT)) // self.GPUS_PER_UNIT
                local_gpu_id = gpu_id % self.GPUS_PER_UNIT
                
                gpu_info = self.clusters[cluster_id][unit_id][local_gpu_id]
                
                # Submit mining task
                future = executor.submit(
                    self._gpu_mine,
                    gpu_info,
                    work_chunks[gpu_id],
                    tensor_arrays[gpu_id]
                )
                futures.append(future)
                
        return futures
        
    def _gpu_mine(self, gpu_info: Dict, work_chunk: bytes, tensor_array: np.ndarray):
        """Mining operation on a single GPU"""
        # Get components
        gpu_chip = gpu_info['chip']
        sms = gpu_info['sms']
        tensor_cores = gpu_info['tensor_cores']
        stream = gpu_info['stream']
        
        # Stream work to tensor cores
        for i, block in enumerate(work_chunk):
            # Use cross-GPU streaming
            stream.write_tensor(
                tensor_array[i],
                block,
                tensor_cores[i % len(tensor_cores)]
            )
            
        # Execute on all streaming multiprocessors
        results = []
        for sm in sms:
            sm_result = sm.execute_mining(
                tensor_cores=tensor_cores,
                tensor_array=tensor_array
            )
            results.extend(sm_result)
            
        return results
        for cluster_id in range(self.TOTAL_CLUSTERS):
            self.clusters[cluster_id] = {}
            for unit_id in range(self.UNITS_PER_CLUSTER):
                self.clusters[cluster_id][unit_id] = []
                for gpu_id in range(self.GPUS_PER_UNIT):
                    # Create GPU with enhanced specs
                    gpu = GPUChip(
                        chip_id=cluster_id * self.UNITS_PER_CLUSTER * self.GPUS_PER_UNIT + unit_id * self.GPUS_PER_UNIT + gpu_id,
                        storage_manager=self.storage,
                        num_sms=self.SMS_PER_GPU,
                        vram_gb=self.VRAM_PER_GPU
                    )
                    self.clusters[cluster_id][unit_id].append(gpu)
                    
                    # Initialize tensor cores for this GPU
                    self._init_tensor_cores(gpu)
                    
    def _init_tensor_cores(self, gpu: GPUChip):
        """Initialize tensor cores for a GPU"""
        gpu_id = gpu.chip_id
        self.tensor_cores[gpu_id] = []
        for sm_id in range(self.SMS_PER_GPU):
            for core_id in range(self.TENSOR_CORES_PER_SM):
                tensor_core = TensorCore(
                    core_id=core_id,
                    sm_id=sm_id,
                    gpu_id=gpu_id,
                    clock_speed=self.SWITCH_FREQ,
                    storage=self.storage
                )
                self.tensor_cores[gpu_id].append(tensor_core)
                
    def get_theoretical_hashrate(self) -> float:
        """Calculate theoretical maximum hash rate"""
        total_cores = (self.GPUS_PER_UNIT * 
                      self.UNITS_PER_CLUSTER * 
                      self.TOTAL_CLUSTERS * 
                      self.SMS_PER_GPU * 
                      self.TENSOR_CORES_PER_SM)
        
        # Each tensor core can do multiple hashes per cycle
        hashes_per_cycle = 256  # SHA-256 optimized
        
        # Calculate hashes per second based on switching frequency
        hash_rate = total_cores * hashes_per_cycle * self.SWITCH_FREQ
        
        return hash_rate
        
    def calculate_mining_performance(self, timeframe_hours: float = 2.0) -> Dict[str, Any]:
        """Calculate mining performance and expected BTC earnings for given timeframe"""
        hashrate = self.get_theoretical_hashrate()
        
        # Calculate network share
        network_share = hashrate / self.NETWORK_HASHRATE
        
        # Calculate blocks in timeframe
        blocks_in_timeframe = (timeframe_hours * 3600) / self.BLOCK_TIME
        
        # Expected blocks found
        expected_blocks = blocks_in_timeframe * network_share
        
        # Expected BTC
        expected_btc = expected_blocks * self.BLOCK_REWARD
        
        return {
            "timeframe_hours": timeframe_hours,
            "hashrate": hashrate,
            "network_share": network_share,
            "expected_blocks": expected_blocks,
            "expected_btc": expected_btc,
            "btc_per_day": expected_btc * (24 / timeframe_hours)
        }
    
    def get_system_specs(self) -> Dict[str, Any]:
        """Get complete system specifications"""
        total_gpus = self.GPUS_PER_UNIT * self.UNITS_PER_CLUSTER * self.TOTAL_CLUSTERS
        total_tensor_cores = total_gpus * self.SMS_PER_GPU * self.TENSOR_CORES_PER_SM
        
        # Get current streaming storage usage
        stream_usage = sum(
            os.path.getsize(f"gpu_storage/gpu_{i}/current.stream")
            for i in range(total_gpus)
            if os.path.exists(f"gpu_storage/gpu_{i}/current.stream")
        ) / (1024 ** 3)  # Convert to GB
        
        # Calculate mining performance for 2 hours
        mining_perf = self.calculate_mining_performance(2.0)
        
        return {
            "total_gpus": total_gpus,
            "total_tensor_cores": total_tensor_cores,
            "streaming_storage": {
                "block_size": self.STREAM_BLOCK_SIZE,
                "current_usage_gb": stream_usage,
                "storage_path": "gpu_storage",
                "stream_mode": "Pure streaming, no pre-allocation"
            },
            "theoretical_hashrate": self.get_theoretical_hashrate(),
            "optical_bandwidth_tbps": self.interconnect.bandwidth_tbps,
            "optical_latency_ns": self.interconnect.latency_ns,
            "switch_frequency_hz": self.SWITCH_FREQ,
            "gate_delay_s": self.GATE_DELAY,
            "mining_performance": mining_perf,
            "parallel_distribution": {
                "max_concurrent_kernels": self.distributor.max_concurrent_kernels,
                "load_threshold": self.distributor.load_threshold,
                "min_chunk_size": self.distributor.min_chunk_size
            }
        }
