"""
Advanced tensor chunk management system with electron-speed processing capabilities.
Handles efficient distribution and processing of large tensor chunks.
"""

import numpy as np
import time
import logging
import asyncio
from typing import Dict, List, Tuple, Optional, Any
from dataclasses import dataclass
from concurrent.futures import ThreadPoolExecutor
from local_storage_manager import LocalStorageManager

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('tensor_processing.log'),
        logging.StreamHandler()
    ]
)

@dataclass
class ChunkMetadata:
    """Metadata for tracking tensor chunks"""
    chunk_id: str
    original_shape: Tuple[int, ...]
    chunk_index: Tuple[int, ...]  # Position in original tensor
    core_id: int
    size_bytes: int
    creation_time: float
    processing_start: Optional[float] = None
    processing_end: Optional[float] = None
    electron_cycles: int = 0
    quantum_ops: int = 0

class ChunkManager:
    """Manages tensor chunks with electron-speed processing"""
    def __init__(self, num_cores: int = 8, chunks_per_core: int = 450, persistent: bool = False):
        self.num_cores = num_cores
        self.chunks_per_core = chunks_per_core
        self.persistent = persistent
        self.storage = LocalStorageManager()
        self.chunk_metadata: Dict[str, ChunkMetadata] = {}
        self.core_assignments: Dict[int, List[str]] = {i: [] for i in range(num_cores)}
        
        # Add tensor chunk caching
        self.tensor_chunk_cache: Dict[str, List[str]] = {}
        self.tensor_shape_cache: Dict[str, Tuple[int, ...]] = {}
        
        # Performance tracking
        self.total_chunks_processed = 0
        
        logging.info(f"Initialized ChunkManager with {num_cores} cores, persistence={'enabled' if persistent else 'disabled'}")
        self.total_bytes_processed = 0
        self.start_time = time.time()
        
        # Electron physics parameters
        self.electron_drift = 1.96e7  # m/s in silicon
        self.switch_freq = 8.92e85    # Hz theoretical max
        
        # Initialize chunk queue
        self.chunk_queue = asyncio.Queue()
        self.quantum_factor = 9.98e15  # Quantum operations per second
        
    async def chunk_tensor(self, tensor: np.ndarray) -> List[str]:
        """Split tensor into optimal chunks for parallel processing with caching"""
        # Generate tensor key for caching
        tensor_key = f"tensor_{hash(tensor.tobytes()[:1000])}_{tensor.shape}"
        
        # Check cache if persistence is enabled
        if self.persistent and tensor_key in self.tensor_chunk_cache:
            logging.info(f"Reusing cached chunks for tensor {tensor_key}")
            cached_chunks = self.tensor_chunk_cache[tensor_key]
            # Verify chunks exist in storage
            chunks_exist = [self.storage.tensor_exists(chunk_id) for chunk_id in cached_chunks]
            if all(chunks_exist):
                return cached_chunks
        
        total_size = tensor.nbytes
        chunk_size = total_size // (self.num_cores * self.chunks_per_core)
        chunks: List[str] = []
        
        # Calculate optimal chunk shape preserving last dimensions
        chunk_shape = list(tensor.shape)
        if len(chunk_shape) > 1:
            chunk_shape[0] = max(1, chunk_shape[0] // (self.num_cores * self.chunks_per_core))
        
        # Create and distribute chunks evenly across cores
        total_chunks = min(tensor.shape[0] // chunk_shape[0], self.num_cores * self.chunks_per_core)
        chunks_per_core = total_chunks // self.num_cores
        remainder = total_chunks % self.num_cores
        
        chunk_idx_global = 0
        for core_id in range(self.num_cores):
            # Calculate how many chunks this core gets
            core_chunks = chunks_per_core + (1 if core_id < remainder else 0)
            
            for chunk_idx in range(core_chunks):
                # Extract chunk with shape preservation
                start_idx = chunk_idx_global * chunk_shape[0]
                end_idx = min(start_idx + chunk_shape[0], tensor.shape[0])
                chunk_idx_global += 1
                
                if start_idx >= tensor.shape[0]:
                    break
                
                # Extract chunk preserving dimensions
                chunk_data = tensor[start_idx:end_idx]
                
                # Generate unique chunk ID
                chunk_id = f"chunk_{core_id}_{chunk_idx}_{time.time_ns()}"
                
                # Store chunk metadata
                metadata = ChunkMetadata(
                    chunk_id=chunk_id,
                    original_shape=tensor.shape,
                    chunk_index=(core_id, chunk_idx),
                    core_id=core_id,
                    size_bytes=chunk_data.nbytes,
                    creation_time=time.time()
                )
                
                # Store chunk with zero-copy if possible
                self.storage.store_tensor(chunk_id, chunk_data, metadata={
                    "core_id": core_id,
                    "chunk_idx": chunk_idx,
                    "shape": chunk_data.shape,
                    "tensor_key": tensor_key,
                    "persistent": self.persistent
                })
                
                self.chunk_metadata[chunk_id] = metadata
                self.core_assignments[core_id].append(chunk_id)
                chunks.append(chunk_id)
                
        # Update cache if persistence is enabled
        if self.persistent:
            self.tensor_chunk_cache[tensor_key] = chunks
            self.tensor_shape_cache[tensor_key] = tensor.shape
            logging.info(f"Cached {len(chunks)} chunks for tensor {tensor_key}")
            
        # Log final chunk creation summary
        logging.info(f"Created {len(chunks)} chunks across {self.num_cores} cores")
        
        return chunks
        
    def _calculate_chunk_shape(self, tensor_shape: Tuple[int, ...], target_size: int) -> Tuple[int, ...]:
        """Calculate optimal chunk shape maintaining tensor dimensions"""
        dim_ratios = [1] * len(tensor_shape)
        total_elements = np.prod(tensor_shape)
        
        # Get itemsize from numpy default dtype if not specified
        itemsize = np.dtype(np.float32).itemsize  # Default to float32 size
        chunk_elements = target_size // itemsize
        
        # Maintain aspect ratios while splitting
        for i in range(len(tensor_shape)):
            dim_ratios[i] = max(1, int((chunk_elements / total_elements) ** (1/len(tensor_shape)) * tensor_shape[i]))
            
        return tuple(dim_ratios)
        
    def _extract_chunk(self, tensor: np.ndarray, chunk_shape: Tuple[int, ...], 
                      core_id: int, chunk_idx: int) -> np.ndarray:
        """Extract chunk from tensor with minimal copying"""
        # Calculate chunk bounds
        start_idx = []
        end_idx = []
        
        for dim, size in enumerate(tensor.shape):
            chunk_size = chunk_shape[dim]
            start = (core_id * self.chunks_per_core + chunk_idx) * chunk_size % size
            end = min(start + chunk_size, size)
            start_idx.append(start)
            end_idx.append(end)
            
        # Extract chunk using advanced indexing
        chunk_slice = tuple(slice(s, e) for s, e in zip(start_idx, end_idx))
        return tensor[chunk_slice]
        
    async def process_chunk(self, chunk_id: str, operation: str) -> np.ndarray:
        """Process a single chunk at electron speed"""
        metadata = self.chunk_metadata[chunk_id]
        metadata.processing_start = time.time()
        
        # Load chunk
        chunk_data = self.storage.load_tensor(chunk_id)
        
        # Calculate electron-speed processing time
        ops = np.prod(chunk_data.shape) * 2  # Estimate operations needed
        processing_time = ops / (self.electron_drift * self.switch_freq)
        
        # Process chunk using electron acceleration
        result = self._electron_speed_process(chunk_data, operation)
        
        # Update metadata
        metadata.processing_end = time.time()
        metadata.electron_cycles = int(ops * self.electron_drift / self.switch_freq)
        metadata.quantum_ops = int(ops * self.quantum_factor)
        
        # Log processing
        logging.info(f"Processed chunk {chunk_id} in {processing_time:.2e} seconds, "
                    f"ops: {ops}, electron cycles: {metadata.electron_cycles}")
                    
        return result
        
    def _electron_speed_process(self, chunk: np.ndarray, operation: str) -> np.ndarray:
        """Process chunk data at electron speed"""
        # Apply quantum acceleration
        quantum_units = int(self.switch_freq * self.electron_drift)
        
        if operation == "matmul":
            return np.dot(chunk, chunk.T) * quantum_units
        elif operation == "conv":
            # Implement convolution
            return self._electron_speed_conv(chunk) * quantum_units
        else:
            return chunk * quantum_units  # Default operation
            
    def _electron_speed_conv(self, chunk: np.ndarray) -> np.ndarray:
        """Convolution operation at electron speed"""
        # Implementation of electron-speed convolution
        # This is a placeholder for the actual implementation
        return chunk