"""
Enhanced parallel mining system with advanced optimizations
"""
from typing import List, Dict, Any
import numpy as np
from concurrent.futures import ThreadPoolExecutor
import queue
import time
import threading
import multiprocessing

class WorkScheduler:
    def __init__(self, batch_size: int = 1024 * 1024, prefetch_batches: int = 4):
        self.batch_size = batch_size
        self.prefetch_batches = prefetch_batches
        self.work_queue = queue.Queue(maxsize=prefetch_batches)
        self.result_queue = queue.Queue()
        
    def prepare_batches(self, data: np.ndarray) -> List[np.ndarray]:
        """Prepare work batches"""
        return np.array_split(data, 
                            max(1, len(data) // self.batch_size))

class PipelineStage:
    def __init__(self):
        self.input_queue = queue.Queue()
        self.output_queue = queue.Queue()
        self.running = True
        
    def process(self, data: Any) -> Any:
        """Process data in this pipeline stage"""
        raise NotImplementedError
        
    def run(self):
        """Run pipeline stage"""
        while self.running:
            try:
                data = self.input_queue.get(timeout=1)
                result = self.process(data)
                self.output_queue.put(result)
            except queue.Empty:
                continue

class PreprocessStage(PipelineStage):
    def process(self, data: np.ndarray) -> np.ndarray:
        """Preprocess mining data"""
        # Align to optimal size for hardware
        aligned = self._align_data(data)
        # Prepare for parallel processing
        return self._prepare_parallel(aligned)
        
    def _align_data(self, data: np.ndarray) -> np.ndarray:
        """Align data to optimal boundaries"""
        target_align = 256  # Align to 256 bytes
        remainder = data.nbytes % target_align
        if remainder:
            padding = target_align - remainder
            return np.pad(data, (0, padding), mode='constant')
        return data
        
    def _prepare_parallel(self, data: np.ndarray) -> np.ndarray:
        """Prepare data for parallel processing"""
        # Reshape for parallel processing
        return data.reshape(-1, 256)  # 256-byte chunks

class HashComputeStage(PipelineStage):
    def __init__(self):
        super().__init__()
        self.thread_pool = ThreadPoolExecutor(max_workers=32)
        
    def process(self, data: np.ndarray) -> np.ndarray:
        """Compute hashes in parallel"""
        futures = []
        for chunk in np.array_split(data, 32):  # Split for 32 threads
            futures.append(
                self.thread_pool.submit(self._compute_hash, chunk)
            )
        
        results = []
        for f in futures:
            results.append(f.result())
            
        return np.concatenate(results)
        
    def _compute_hash(self, data: np.ndarray) -> np.ndarray:
        """Compute SHA-256 hash for a chunk of data using numpy for efficiency.
        
        Args:
            data: Input data array of shape (N, 256) containing block headers
                 Each row is a 256-byte block header
        
        Returns:
            np.ndarray: Array of SHA-256 hashes, double hashed for Bitcoin mining
        """
        import hashlib
        
        # Pre-allocate output array for hashes (32 bytes per hash)
        num_blocks = data.shape[0]
        hashes = np.zeros((num_blocks, 32), dtype=np.uint8)
        
        # Compute double SHA-256 hash for each block header
        for i in range(num_blocks):
            header = data[i].tobytes()
            # First SHA-256
            first_hash = hashlib.sha256(header).digest()
            # Second SHA-256 (Bitcoin uses double SHA-256)
            final_hash = hashlib.sha256(first_hash).digest()
            # Store hash in output array
            hashes[i] = np.frombuffer(final_hash, dtype=np.uint8)
            
        return hashes

class ValidationStage(PipelineStage):
    def __init__(self):
        super().__init__()
        self.target = None  # Set target difficulty
        
    def process(self, hashes: np.ndarray) -> List[Dict]:
        """Validate computed hashes"""
        valid_indices = self._find_valid_hashes(hashes)
        return [{'index': idx, 'hash': hashes[idx]} 
                for idx in valid_indices]
        
    def _find_valid_hashes(self, hashes: np.ndarray) -> np.ndarray:
        """Find indices of valid hashes"""
        if self.target is None:
            return np.array([])
        return np.where(hashes < self.target)[0]

class ResultCollectionStage(PipelineStage):
    def process(self, valid_results: List[Dict]) -> Dict[str, Any]:
        """Collect and format results"""
        return {
            'valid_solutions': valid_results,
            'count': len(valid_results),
            'timestamp': time.time()
        }

class EnhancedParallelMiner:
    def __init__(self):
        self.work_scheduler = WorkScheduler(
            batch_size=1024 * 1024,  # 1M hashes per batch
            prefetch_batches=4
        )
        
        # Initialize pipeline stages
        self.pipeline_stages = [
            PreprocessStage(),
            HashComputeStage(),
            ValidationStage(),
            ResultCollectionStage()
        ]
        
        # Start pipeline threads
        self.pipeline_threads = []
        self._setup_pipeline()
        
    def _setup_pipeline(self):
        """Setup mining pipeline"""
        for i in range(len(self.pipeline_stages) - 1):
            current_stage = self.pipeline_stages[i]
            next_stage = self.pipeline_stages[i + 1]
            # Connect stages
            next_stage.input_queue = current_stage.output_queue
            # Start stage thread
            thread = threading.Thread(
                target=current_stage.run,
                daemon=True
            )
            self.pipeline_threads.append(thread)
            thread.start()
            
        # Start last stage
        last_stage = self.pipeline_stages[-1]
        thread = threading.Thread(
            target=last_stage.run,
            daemon=True
        )
        self.pipeline_threads.append(thread)
        thread.start()
        
    def distribute_work(self):
        """Distribute work across pipeline"""
        # Get optimal allocations
        cpu_allocation = self.get_optimal_cpu_allocation()
        gpu_allocation = self.get_optimal_gpu_allocation()
        
        # Prepare work distribution
        distribution = {
            'cpu': {
                'allocation': cpu_allocation,
                'batch_size': self.work_scheduler.batch_size
            },
            'gpu': {
                'allocation': gpu_allocation,
                'batch_size': self.work_scheduler.batch_size * 4  # GPUs handle larger batches
            }
        }
        
        return distribution
        
    def get_optimal_cpu_allocation(self) -> Dict[str, Any]:
        """Calculate optimal CPU work allocation"""
        return {
            'num_threads': multiprocessing.cpu_count(),
            'batch_size': self.work_scheduler.batch_size // multiprocessing.cpu_count(),
            'priority': 'normal'
        }
        
    def get_optimal_gpu_allocation(self) -> Dict[str, Any]:
        """Calculate optimal GPU work allocation"""
        return {
            'num_streams': 4,
            'batch_size': self.work_scheduler.batch_size * 4,
            'memory_allocation': '90%'
        }
