"""
Tensor Core based Bitcoin mining implementation
Uses tensor cores for parallel SHA256 computation with zero RAM footprint
"""

import numpy as np
import time
import hashlib
from typing import Tuple, Optional, List
from tensor_core import TensorCore
from simple_parallel_distributor import ParallelArrayDistributor
from electron_speed import max_switch_freq, drift_velocity, transit_time, speed_of_light_silicon
from disk_storage_manager import DiskStorageManager

class TensorMiner:
    def __init__(self, 
                 num_tensor_cores: int = 8000,
                 cores_per_sm: int = 3000,
                 memory_size_gb: Optional[float] = None,
                 bandwidth_tbps: float = 10000):
        """
        Initialize tensor mining system
        
        Args:
            num_tensor_cores: Number of tensor cores to use
            cores_per_sm: Number of cores per streaming multiprocessor
            memory_size_gb: Virtual memory size in GB (None = unlimited)
            bandwidth_tbps: Memory bandwidth in TB/s
        """
        self.num_tensor_cores = num_tensor_cores
        self.cores_per_sm = cores_per_sm
        self.num_sms = num_tensor_cores // cores_per_sm
        self.memory_size = memory_size_gb * 1024 * 1024 * 1024 if memory_size_gb else None
        self.bandwidth = bandwidth_tbps * 1024 * 1024 * 1024 * 1024  # Convert to bytes/s
        
        # Initialize tensor cores
        self.tensor_cores = [
            TensorCore(core_id=i, sm_id=i // cores_per_sm)
            for i in range(num_tensor_cores)
        ]
        
        # Initialize parallel distributor
        self.distributor = ParallelArrayDistributor(
            num_sms=self.num_sms,
            cores_per_sm=cores_per_sm
        )
        
        # Initialize storage manager for mining data
        self.storage = DiskStorageManager("storage/tensor_mining")
        
        # Performance tracking
        self.start_time = None
        self.total_hashes = 0
        self.hash_rates = []
        
    def _prepare_mining_batch(self, header: bytes, start_nonce: int, batch_size: int) -> np.ndarray:
        """Prepare a batch of block headers with different nonces"""
        # Create array of nonces
        nonces = np.arange(start_nonce, start_nonce + batch_size, dtype=np.uint32)
        
        # Convert header to numpy array and repeat for each nonce
        header_array = np.frombuffer(header, dtype=np.uint8)
        headers = np.tile(header_array, (batch_size, 1))
        
        # Insert nonces at the correct position (last 4 bytes)
        nonce_bytes = nonces.view(np.uint8).reshape(-1, 4)
        headers[:, -4:] = nonce_bytes
        
        return headers
        
    def mine_batch(self, header: bytes, target: bytes, start_nonce: int, batch_size: int) -> Tuple[Optional[int], int]:
        """
        Mine a batch of nonces using tensor cores
        
        Returns:
            Tuple of (winning nonce if found, else None, number of hashes computed)
        """
        # Prepare mining data
        headers = self._prepare_mining_batch(header, start_nonce, batch_size)
        target_array = np.frombuffer(target, dtype=np.uint8)
        
        # Distribute work across tensor cores
        distributed_headers = self.distributor.distribute(headers)
        hashes = np.zeros((batch_size, 32), dtype=np.uint8)
        
        # Process on tensor cores in parallel
        for core_id, chunk in enumerate(distributed_headers):
            if chunk.size > 0:  # If this core has work
                core_result = self.tensor_cores[core_id].process_batch(chunk)
                start_idx = core_id * (batch_size // self.num_tensor_cores)
                end_idx = start_idx + core_result.shape[0]
                hashes[start_idx:end_idx] = core_result
        
        # Check results against target
        results = hashes < target_array
        winning_indices = np.where(np.all(results, axis=1))[0]
        
        # Update stats
        self.total_hashes += batch_size
        if self.start_time is None:
            self.start_time = time.time()
        elapsed = time.time() - self.start_time
        current_rate = batch_size / elapsed if elapsed > 0 else 0
        self.hash_rates.append(current_rate)
        
        if len(winning_indices) > 0:
            return start_nonce + winning_indices[0], batch_size
        return None, batch_size
        
    def get_performance_stats(self) -> dict:
        """Get current mining performance statistics"""
        if self.start_time is None:
            return {"status": "Not started"}
            
        elapsed = time.time() - self.start_time
        avg_hashrate = self.total_hashes / elapsed if elapsed > 0 else 0
        current_hashrate = self.hash_rates[-1] if self.hash_rates else 0
        
        # Calculate theoretical peak performance
        electron_freq = max_switch_freq
        theoretical_max = electron_freq * self.num_tensor_cores * 256  # 256-bit SHA ops
        
        return {
            "total_hashes": self.total_hashes,
            "elapsed_time": elapsed,
            "average_hashrate": avg_hashrate,
            "current_hashrate": current_hashrate,
            "theoretical_max_hashrate": theoretical_max,
            "efficiency": (avg_hashrate / theoretical_max) if theoretical_max > 0 else 0,
            "num_tensor_cores": self.num_tensor_cores,
            "cores_per_sm": self.cores_per_sm,
            "memory_bandwidth_tbps": self.bandwidth / (1024 ** 4)
        }