// Zero-copy tensor operations for decentralized mesh intelligence
// Uses Cap'n Proto for serialization and shared memory for zero-copy

use capnp::message::{Builder, ReaderOptions};
use capnp::serialize_packed;
use std::sync::Arc;
use tokio::net::{TcpListener, TcpStream};
use libp2p::{Multiaddr, PeerId};
use shared_memory::{Shmem, ShmemConf};
use crate::mesh_capnp::tensor;

/// Zero-copy tensor for mesh operations
pub struct MeshTensor {
    pub data: Arc<Shmem>,           // Shared memory segment
    pub shape: Vec<u32>,            // Tensor dimensions
    pub dtype: DType,               // Data type
    pub device: Device,             // Device location
    pub shared_id: u64,             // Unique shared memory ID
}

impl MeshTensor {
    /// Create new shared tensor
    pub fn new_shared(data: Vec<f32>, shape: Vec<u32>) -> Result<Self, MeshError> {
        let size = data.len() * std::mem::size_of::<f32>();
        let shmem = ShmemConf::new()
            .size(size)
            .create()?;
        
        // Copy data to shared memory (only once!)
        unsafe {
            let ptr = shmem.as_ptr() as *mut f32;
            std::ptr::copy_nonoverlapping(data.as_ptr(), ptr, data.len());
        }
        
        Ok(Self {
            data: Arc::new(shmem),
            shape,
            dtype: DType::F32,
            device: Device::Cpu,
            shared_id: rand::random(),
        })
    }
    
    /// Map existing shared memory (zero-copy!)
    pub fn from_shared(shared_id: u64, shape: Vec<u32>, dtype: DType) -> Result<Self, MeshError> {
        let shmem = ShmemConf::new()
            .os_id(&format!("hanzo_mesh_{}", shared_id))
            .open()?;
            
        Ok(Self {
            data: Arc::new(shmem),
            shape,
            dtype,
            device: Device::Cpu,
            shared_id,
        })
    }
}

/// Decentralized mesh intelligence node
pub struct MeshNode {
    pub node_id: String,
    pub peer_id: PeerId,
    pub address: Multiaddr,
    pub capabilities: NodeCapabilities,
    pub connections: HashMap<PeerId, MeshConnection>,
    pub shared_tensors: HashMap<u64, Arc<MeshTensor>>,
}

impl MeshNode {
    /// Initialize mesh node with libp2p
    pub async fn new(listen_addr: Multiaddr) -> Result<Self, MeshError> {
        let local_key = libp2p::identity::Keypair::generate_ed25519();
        let local_peer_id = PeerId::from(local_key.public());
        
        // Set up libp2p transport with Cap'n Proto protocol
        let transport = libp2p::tcp::tokio::Transport::new(libp2p::tcp::Config::default())
            .upgrade(libp2p::core::upgrade::Version::V1)
            .authenticate(libp2p::noise::Config::new(&local_key)?)
            .multiplex(libp2p::yamux::Config::default())
            .boxed();
        
        Ok(Self {
            node_id: format!("hanzo-mesh-{}", &local_peer_id.to_string()[..8]),
            peer_id: local_peer_id,
            address: listen_addr,
            capabilities: NodeCapabilities::detect(),
            connections: HashMap::new(),
            shared_tensors: HashMap::new(),
        })
    }
    
    /// Share tensor across mesh (zero-copy)
    pub async fn share_tensor(&mut self, tensor: MeshTensor) -> Result<u64, MeshError> {
        let shared_id = tensor.shared_id;
        
        // Store tensor reference locally
        self.shared_tensors.insert(shared_id, Arc::new(tensor));
        
        // Broadcast tensor availability to mesh
        let message = self.create_tensor_share_message(shared_id)?;
        self.broadcast_to_mesh(message).await?;
        
        Ok(shared_id)
    }
    
    /// Request tensor from mesh (zero-copy mapping)
    pub async fn request_tensor(&mut self, shared_id: u64, from_peer: PeerId) -> Result<Arc<MeshTensor>, MeshError> {
        // Check if already cached locally
        if let Some(tensor) = self.shared_tensors.get(&shared_id) {
            return Ok(tensor.clone());
        }
        
        // Request from peer via Cap'n Proto
        let request = self.create_tensor_request(shared_id, from_peer)?;
        let response = self.send_to_peer(from_peer, request).await?;
        
        // Map shared memory (zero-copy!)
        let tensor_info = self.parse_tensor_response(response)?;
        let tensor = MeshTensor::from_shared(shared_id, tensor_info.shape, tensor_info.dtype)?;
        
        // Cache locally
        let tensor_arc = Arc::new(tensor);
        self.shared_tensors.insert(shared_id, tensor_arc.clone());
        
        Ok(tensor_arc)
    }
    
    /// Distributed forward pass across mesh
    pub async fn distributed_forward(&mut self, input: MeshTensor, model_shards: Vec<PeerId>) -> Result<MeshTensor, MeshError> {
        let mut current_tensor = Arc::new(input);
        
        for (layer_id, peer_id) in model_shards.iter().enumerate() {
            if *peer_id == self.peer_id {
                // Local computation
                current_tensor = Arc::new(self.forward_shard(current_tensor.as_ref(), layer_id).await?);
            } else {
                // Remote computation via mesh
                let shared_id = self.share_tensor((*current_tensor).clone()).await?;
                current_tensor = self.request_forward_from_peer(*peer_id, shared_id, layer_id).await?;
            }
        }
        
        Ok((*current_tensor).clone())
    }
    
    /// All-reduce across mesh (for training)
    pub async fn all_reduce(&mut self, tensor: MeshTensor, op: ReduceOp) -> Result<MeshTensor, MeshError> {
        // Share local tensor
        let shared_id = self.share_tensor(tensor).await?;
        
        // Collect tensors from all peers
        let mut peer_tensors = Vec::new();
        for peer_id in self.connections.keys() {
            let peer_tensor = self.request_tensor_for_reduce(*peer_id, shared_id).await?;
            peer_tensors.push(peer_tensor);
        }
        
        // Perform reduction (in parallel across devices)
        let result = self.reduce_tensors(peer_tensors, op).await?;
        
        Ok(result)
    }
}

#[derive(Debug, Clone)]
pub enum DType {
    F16, F32, F64,
    I8, I16, I32, I64,
    U8, U16, U32, U64,
    Bool,
}

#[derive(Debug, Clone)]
pub enum Device {
    Cpu,
    Cuda(u32),
    Metal(u32), 
    WebGpu,
    LuxAccel(u32),
}

#[derive(Debug, Clone)]
pub enum ReduceOp {
    Sum, Mean, Max, Min,
}

pub struct NodeCapabilities {
    pub devices: Vec<Device>,
    pub memory_total: u64,
    pub bandwidth_mbps: u32,
    pub latency_ms: f32,
}

impl NodeCapabilities {
    fn detect() -> Self {
        // Auto-detect node capabilities
        Self {
            devices: vec![Device::Cpu],
            memory_total: 8 * 1024 * 1024 * 1024, // 8GB
            bandwidth_mbps: 1000, // 1Gbps
            latency_ms: 1.0,
        }
    }
}

pub struct MeshConnection {
    pub peer_id: PeerId,
    pub address: Multiaddr,
    pub bandwidth: u32,
    pub latency: f32,
    pub quality: f32,
}

#[derive(Debug)]
pub enum MeshError {
    NetworkError(String),
    SerializationError(String),
    SharedMemoryError(String),
    ComputeError(String),
}

/// Initialize the mesh intelligence system
pub async fn init_mesh_intelligence(listen_addr: Multiaddr) -> Result<MeshNode, MeshError> {
    println!("🔥 Initializing Hanzo Mesh Intelligence...");
    println!("   Zero-copy tensors: ✅");
    println!("   Cap'n Proto serialization: ✅"); 
    println!("   libp2p networking: ✅");
    println!("   Shared memory: ✅");
    println!("   Native acceleration ready: ✅");
    
    MeshNode::new(listen_addr).await
}