from dataclasses import dataclass
from typing import List, Dict, Any, Callable
import numpy as np
from threading import Lock

@dataclass
class KernelConfig:
    """Configuration for a CUDA-like kernel launch"""
    block_dim: tuple[int, int, int]  # threads per block (x,y,z)
    grid_dim: tuple[int, int, int]   # blocks per grid (x,y,z)
    shared_memory_size: int = 0      # bytes of shared memory per block
    
class ThreadIdx:
    """Thread index within a block"""
    def __init__(self, x: int, y: int, z: int):
        self.x = x
        self.y = y
        self.z = z
        
class BlockIdx:
    """Block index within the grid"""
    def __init__(self, x: int, y: int, z: int):
        self.x = x
        self.y = y
        self.z = z

class Warp:
    """Represents a group of 32 threads that execute in lockstep"""
    WARP_SIZE = 32
    
    def __init__(self, warp_id: int, threads: List[ThreadIdx]):
        self.warp_id = warp_id
        self.threads = threads
        self.active_mask = (1 << len(threads)) - 1  # All threads active initially
        
    def synchronize(self):
        """Synchronize all threads in the warp"""
        pass  # Hardware handled in real GPU
        
    def vote_all(self, predicate: bool) -> bool:
        """Returns true if predicate is true for all active threads"""
        return all(predicate for _ in range(len(self.threads)))
        
    def vote_any(self, predicate: bool) -> bool:
        """Returns true if predicate is true for any active thread"""
        return any(predicate for _ in range(len(self.threads)))

class Block:
    """Represents a thread block with shared memory"""
    def __init__(self, block_idx: BlockIdx, dim: tuple[int, int, int], shared_mem_size: int):
        self.block_idx = block_idx
        self.dim = dim
        self.shared_memory = SharedMemory(shared_mem_size)
        self.warps: List[Warp] = []
        self._create_warps()
        
    def _create_warps(self):
        """Organize threads into warps"""
        threads = []
        total_threads = self.dim[0] * self.dim[1] * self.dim[2]
        
        for idx in range(total_threads):
            # Convert linear index to 3D
            z = idx // (self.dim[0] * self.dim[1])
            y = (idx % (self.dim[0] * self.dim[1])) // self.dim[0]
            x = idx % self.dim[0]
            threads.append(ThreadIdx(x, y, z))
            
            if len(threads) == Warp.WARP_SIZE or idx == total_threads - 1:
                self.warps.append(Warp(len(self.warps), threads))
                threads = []
                
    def synchronize(self):
        """Synchronize all threads in the block"""
        for warp in self.warps:
            warp.synchronize()

class SharedMemory:
    """Represents shared memory accessible by all threads in a block"""
    def __init__(self, size_bytes: int):
        self.size = size_bytes
        self.data = bytearray(size_bytes)
        self.lock = Lock()
        
    def read(self, offset: int, size: int) -> bytearray:
        with self.lock:
            return self.data[offset:offset + size]
            
    def write(self, offset: int, data: bytearray):
        with self.lock:
            self.data[offset:offset + len(data)] = data

class KernelFunction:
    """Wrapper for a kernel function"""
    def __init__(self, func: Callable):
        self.func = func
        self.shared_memory_size = 0
        
    def configure(self, shared_memory_size: int = 0):
        """Configure kernel properties"""
        self.shared_memory_size = shared_memory_size
        return self
        
    def __call__(self, *args, **kwargs):
        """Execute the kernel function"""
        return self.func(*args, **kwargs)

def launch_kernel(kernel_func: KernelFunction, config: KernelConfig, *args):
    """Launch a kernel with the specified configuration"""
    total_blocks = config.grid_dim[0] * config.grid_dim[1] * config.grid_dim[2]
    
    # Create blocks
    blocks = []
    for block_idx in range(total_blocks):
        # Convert linear index to 3D
        bz = block_idx // (config.grid_dim[0] * config.grid_dim[1])
        by = (block_idx % (config.grid_dim[0] * config.grid_dim[1])) // config.grid_dim[0]
        bx = block_idx % config.grid_dim[0]
        
        block = Block(
            BlockIdx(bx, by, bz),
            config.block_dim,
            config.shared_memory_size
        )
        blocks.append(block)
    
    # Execute kernel on each block
    for block in blocks:
        for warp in block.warps:
            for thread in warp.threads:
                kernel_func(block, thread, *args)

def kernel(func: Callable) -> KernelFunction:
    """Decorator to mark a function as a kernel"""
    return KernelFunction(func)