"""
GPU Mining Manager for distributed mining operations across multiple GPUs
Utilizes tensor cores, streaming multiprocessors, and parallel distribution
"""

from typing import Dict, List, Optional, Tuple, Any
import numpy as np
from virtual_gpu_driver.src.driver_api import VirtualGPUDriver
from gpu_parallel_distributor import GPUParallelDistributor
from streaming_multiprocessor import StreamingMultiprocessor
from tensor_core import TensorCoreArray
from cross_gpu_stream import CrossGPUStreamManager

class GPUMiningManager:
    def __init__(self, num_gpus: int = 10):
        self.num_gpus = num_gpus
        self.gpu_driver = VirtualGPUDriver()
        self.parallel_distributor = GPUParallelDistributor(num_gpus=num_gpus)
        self.stream_manager = CrossGPUStreamManager()
        self.tensor_cores = {}
        self.streaming_multiprocessors = {}
        
        # Initialize GPU components
        self._initialize_gpu_components()
        
    def _initialize_gpu_components(self):
        """Initialize all GPU components for mining"""
        for gpu_id in range(self.num_gpus):
            # Initialize tensor cores for this GPU
            self.tensor_cores[gpu_id] = TensorCoreArray(
                num_cores=16,  # 16 tensor cores per GPU
                gpu_id=gpu_id
            )
            
            # Initialize streaming multiprocessors
            self.streaming_multiprocessors[gpu_id] = [
                StreamingMultiprocessor(
                    sm_id=i,
                    chip_id=gpu_id,
                    num_cores=256  # 256 CUDA cores per SM
                ) for i in range(64)  # 64 SMs per GPU
            ]

    def distribute_mining_task(self, block_data: bytes, target: bytes) -> Dict[str, Any]:
        """Distribute mining task across all available GPUs"""
        # Split work across GPUs
        chunks = self._split_mining_work(block_data)
        
        # Prepare parallel operations
        operations = []
        for gpu_id, chunk in enumerate(chunks):
            op = {
                'type': 'mining',
                'gpu_id': gpu_id,
                'data': chunk,
                'target': target,
                'tensor_ops': self._get_tensor_operations(chunk),
                'sm_ops': self._get_sm_operations(chunk)
            }
            operations.append(op)

        # Distribute operations across GPUs
        distributed_ops = self.parallel_distributor.distribute_operation({
            'type': 'mining_batch',
            'operations': operations,
            'synchronize': True
        })

        return distributed_ops

    def _split_mining_work(self, block_data: bytes) -> List[bytes]:
        """Split mining work into chunks for parallel processing"""
        chunk_size = len(block_data) // self.num_gpus
        return [block_data[i:i+chunk_size] for i in range(0, len(block_data), chunk_size)]

    def _get_tensor_operations(self, data: bytes) -> List[Dict]:
        """Define tensor core operations for mining using matrix-based SHA-256"""
        # Convert input data to matrix form for tensor operations
        data_matrix = self._prepare_sha256_matrix(data)
        
        return [
            # Message schedule matrix transformation (W[i] calculation)
            {
                'op': 'message_schedule_transform',
                'input': data_matrix,
                'matrix_size': 64,  # 64 words in message schedule
                'word_size': 32,    # 32-bit words
                'rounds': 64        # SHA-256 rounds
            },
            # Compression function matrix operations
            {
                'op': 'compression_matrix_ops',
                'input': data_matrix,
                'state_size': 8,    # 8 state variables (a,b,c,d,e,f,g,h)
                'parallel_blocks': 256  # Process multiple blocks in parallel
            },
            # Parallel nonce matrix search
            {
                'op': 'nonce_matrix_search',
                'input': data_matrix,
                'search_space': 2**32,
                'parallel_attempts': 1024  # Search 1024 nonces simultaneously
            }
        ]

    def _prepare_sha256_matrix(self, data: bytes) -> np.ndarray:
        """Prepare input data as matrices for SHA-256 tensor operations"""
        # Convert input to 32-bit words
        words = np.frombuffer(data, dtype=np.uint32)
        
        # Pad to required length
        if len(words) % 16 != 0:
            padding_length = 16 - (len(words) % 16)
            words = np.pad(words, (0, padding_length), 'constant')
        
        # Reshape into matrix form for parallel processing
        # Each row represents a message block
        # Each column represents a 32-bit word
        matrix = words.reshape(-1, 16)
        
        # Add extra dimension for parallel nonce attempts
        # Shape becomes (batch_size, block_words, word_bits)
        matrix = np.expand_dims(matrix, axis=0)
        matrix = np.tile(matrix, (1024, 1, 1))  # Process 1024 nonces in parallel
        
        return matrix

    def _get_sm_operations(self, data: bytes) -> List[Dict]:
        """Define streaming multiprocessor operations for mining"""
        return [
            {
                'op': 'hash_computation',
                'input': data,
                'algorithm': 'sha256d',
                'batch_size': 1024
            },
            {
                'op': 'nonce_verification',
                'batch_size': 1024,
                'parallel_threads': 256
            }
        ]

    def execute_mining_round(self, block_template: bytes, target: bytes) -> Tuple[Optional[int], int]:
        """Execute a complete mining round using all GPU resources"""
        # Distribute the mining task
        distributed_ops = self.distribute_mining_task(block_template, target)
        
        # Track mining stats
        hash_rate = 0
        best_nonce = None
        
        # Process results from each GPU
        for op in distributed_ops:
            gpu_id = op['gpu_id']
            
            # Execute tensor core operations
            tensor_results = self.tensor_cores[gpu_id].execute_batch(op['tensor_ops'])
            
            # Execute SM operations in parallel
            sm_results = []
            for sm in self.streaming_multiprocessors[gpu_id]:
                sm_results.extend(sm.execute_operations(op['sm_ops']))
            
            # Analyze results
            for result in tensor_results + sm_results:
                if result.get('valid_solution'):
                    best_nonce = result['nonce']
                hash_rate += result['hashes_processed']

        return best_nonce, hash_rate

    def get_mining_stats(self) -> Dict[str, Any]:
        """Get mining statistics from all GPUs"""
        stats = {
            'total_hash_rate': 0,
            'gpu_stats': {},
            'tensor_core_utilization': {},
            'sm_utilization': {}
        }
        
        for gpu_id in range(self.num_gpus):
            gpu_stats = self.gpu_driver.get_gpu_stats(gpu_id)
            stats['gpu_stats'][gpu_id] = {
                'temperature': gpu_stats['temperature'],
                'power_usage': gpu_stats['power_usage'],
                'memory_used': gpu_stats['memory_used'],
                'core_utilization': gpu_stats['core_utilization']
            }
            
            # Get tensor core stats
            stats['tensor_core_utilization'][gpu_id] = \
                self.tensor_cores[gpu_id].get_utilization_stats()
            
            # Get SM stats
            stats['sm_utilization'][gpu_id] = {
                sm_id: sm.get_utilization_stats()
                for sm_id, sm in enumerate(self.streaming_multiprocessors[gpu_id])
            }
            
        return stats
