object

defmodule Object.DistributedTraining do
  @moduledoc """
  Distributed Low-Communication (DiLoCo) Training implementation for AAOS.
  
  Implements the DiLoCo algorithm from "DiLoCo: Distributed Low-Communication Training of Language Models"
  (Douillard et al., 2023) as a specialized Object subtype within the AAOS framework.
  
  This module enables training of large language models and other neural networks across
  islands of devices that are poorly connected, requiring minimal communication while
  maintaining performance comparable to fully synchronous training.
  
  ## Key Features
  
  - **Low Communication**: Communicates only every H steps (hundreds/thousands)
  - **Federated Architecture**: Each worker operates on its own data island
  - **Fault Tolerance**: Byzantine resistance and graceful degradation
  - **Heterogeneous Hardware**: Different islands can use different device types
  - **Performance**: 500x less communication than synchronous training
  - **Integration**: Full AAOS object lifecycle and coordination
  
  ## DiLoCo Algorithm
  
  The algorithm consists of two optimization loops:
  1. **Inner Optimization**: Local AdamW updates for H steps
  2. **Outer Optimization**: Global parameter averaging with Nesterov momentum
  
  ## Mathematical Foundation
  
  For T outer steps and H inner steps per worker:
  - Total training steps: N = T × H
  - Communication frequency: Every H steps
  - Workers: k islands of devices
  - Outer gradient: Δ^(t) = (1/k) ∑ᵢ (θ^(t-1) - θᵢ^(t))
  """

  use GenServer
  require Logger

  alias Object.{DistributedRegistry, NetworkTransport, NetworkProtocol}
  alias Object.TransferLearning

  defstruct [
    :object_id,
    :worker_id,
    :global_model_state,
    :local_model_state,
    :optimizer_config,
    :training_config,
    :data_shard,
    :communication_state,
    :performance_metrics,
    :fault_tolerance_state,
    :synchronization_barrier,
    :outer_optimizer_state,
    :inner_optimizer_state,
    :step_counters,
    :coordination_service
  ]

  @type t :: %__MODULE__{
    object_id: String.t(),
    worker_id: worker_id(),
    global_model_state: model_state(),
    local_model_state: model_state(),
    optimizer_config: optimizer_config(),
    training_config: training_config(),
    data_shard: data_shard(),
    communication_state: communication_state(),
    performance_metrics: performance_metrics(),
    fault_tolerance_state: fault_tolerance_state(),
    synchronization_barrier: synchronization_barrier(),
    outer_optimizer_state: optimizer_state(),
    inner_optimizer_state: optimizer_state(),
    step_counters: step_counters(),
    coordination_service: pid()
  }

  @type worker_id :: String.t()
  @type model_state :: %{
    parameters: %{String.t() => tensor()},
    metadata: %{
      architecture: String.t(),
      layer_count: integer(),
      parameter_count: integer(),
      last_updated: DateTime.t()
    }
  }

  @type tensor :: %{
    shape: [integer()],
    data: binary(),
    dtype: :float32 | :float16 | :bfloat16
  }

  @type optimizer_config :: %{
    inner_optimizer: :adamw | :adam | :sgd,
    outer_optimizer: :nesterov_momentum | :sgd | :adam,
    learning_rate: float(),
    momentum: float(),
    weight_decay: float(),
    beta1: float(),
    beta2: float(),
    epsilon: float()
  }

  @type training_config :: %{
    inner_steps: integer(),
    outer_steps: integer(),
    batch_size: integer(),
    gradient_clipping: float(),
    communication_frequency: integer(),
    fault_tolerance_threshold: float(),
    checkpoint_frequency: integer()
  }

  @type data_shard :: %{
    shard_id: String.t(),
    data_path: String.t(),
    total_samples: integer(),
    current_position: integer(),
    preprocessing_config: map()
  }

  @type communication_state :: %{
    last_sync: DateTime.t(),
    pending_gradients: map(),
    sync_barrier_count: integer(),
    communication_overhead: float(),
    bandwidth_usage: float()
  }

  @type performance_metrics :: %{
    training_loss: float(),
    validation_loss: float(),
    throughput: float(),
    communication_efficiency: float(),
    convergence_rate: float(),
    wall_clock_time: integer(),
    compute_utilization: float()
  }

  @type fault_tolerance_state :: %{
    failed_workers: [worker_id()],
    backup_checkpoints: %{String.t() => model_state()},
    consensus_state: consensus_state(),
    health_status: :healthy | :degraded | :critical
  }

  @type consensus_state :: %{
    algorithm: :pbft | :raft | :practical_bft,
    view_number: integer(),
    leader: worker_id() | nil,
    votes: %{worker_id() => vote()},
    committed_steps: integer()
  }

  @type vote :: %{
    step: integer(),
    model_hash: binary(),
    timestamp: DateTime.t(),
    signature: binary()
  }

  @type synchronization_barrier :: %{
    barrier_id: String.t(),
    expected_workers: [worker_id()],
    arrived_workers: [worker_id()],
    timeout: integer(),
    start_time: DateTime.t()
  }

  @type optimizer_state :: %{
    type: atom(),
    state: map(),
    step_count: integer(),
    accumulated_gradients: map()
  }

  @type step_counters :: %{
    inner_step: integer(),
    outer_step: integer(),
    total_steps: integer(),
    communication_rounds: integer()
  }

  # Client API

  @doc """
  Creates a new DiLoCo distributed training object.
  """
  def new(opts \\ []) do
    object_id = Keyword.get(opts, :object_id, generate_object_id())
    worker_id = Keyword.get(opts, :worker_id, generate_worker_id())
    
    %__MODULE__{
      object_id: object_id,
      worker_id: worker_id,
      global_model_state: init_model_state(opts),
      local_model_state: init_model_state(opts),
      optimizer_config: init_optimizer_config(opts),
      training_config: init_training_config(opts),
      data_shard: init_data_shard(opts),
      communication_state: init_communication_state(),
      performance_metrics: init_performance_metrics(),
      fault_tolerance_state: init_fault_tolerance_state(),
      synchronization_barrier: nil,
      outer_optimizer_state: init_optimizer_state(:nesterov_momentum),
      inner_optimizer_state: init_optimizer_state(:adamw),
      step_counters: %{
        inner_step: 0,
        outer_step: 0,
        total_steps: 0,
        communication_rounds: 0
      },
      coordination_service: nil
    }
  end

  @doc """
  Starts the distributed training object as a GenServer.
  """
  def start_link(distributed_trainer, opts \\ []) do
    GenServer.start_link(__MODULE__, distributed_trainer, opts)
  end

  @doc """
  Initializes a training coalition of distributed workers.
  """
  def create_training_coalition(workers, coalition_config) do
    coalition_id = generate_coalition_id()
    
    # Start coordination service
    {:ok, coordinator} = Object.CoordinationService.start_link(
      coalition_id: coalition_id,
      members: workers,
      coordination_strategy: :distributed_consensus,
      fault_tolerance: :byzantine_resistant
    )
    
    # Initialize each worker with coordination service
    Enum.map(workers, fn worker ->
      %{worker | coordination_service: coordinator}
    end)
  end

  @doc """
  Executes the DiLoCo training algorithm.
  """
  def train(pid, training_data) do
    GenServer.call(pid, {:train, training_data}, :infinity)
  end

  @doc """
  Performs a single outer training step (T outer iterations).
  """
  def outer_step(pid) do
    GenServer.call(pid, :outer_step, :infinity)
  end

  @doc """
  Performs H inner training steps on local data.
  """
  def inner_steps(pid, data_batch) do
    GenServer.call(pid, {:inner_steps, data_batch})
  end

  @doc """
  Synchronizes with other workers in the coalition.
  """
  def synchronize(pid) do
    GenServer.call(pid, :synchronize, 30_000)
  end

  @doc """
  Gets current training metrics.
  """
  def get_metrics(pid) do
    GenServer.call(pid, :get_metrics)
  end

  @doc """
  Saves a training checkpoint.
  """
  def save_checkpoint(pid, checkpoint_path) do
    GenServer.call(pid, {:save_checkpoint, checkpoint_path})
  end

  @doc """
  Loads a training checkpoint.
  """
  def load_checkpoint(pid, checkpoint_path) do
    GenServer.call(pid, {:load_checkpoint, checkpoint_path})
  end

  # GenServer Callbacks

  @impl true
  def init(distributed_trainer) do
    # Register with distributed registry
    :ok = DistributedRegistry.register_object(
      distributed_trainer.object_id, 
      distributed_trainer
    )
    
    # Start performance monitoring
    schedule_metrics_collection()
    
    # Initialize communication channels
    {:ok, transport} = NetworkTransport.start_link()
    
    Logger.info("DiLoCo worker #{distributed_trainer.worker_id} initialized")
    
    {:ok, %{distributed_trainer | transport: transport}}
  end

  @impl true
  def handle_call({:train, training_data}, _from, state) do
    Logger.info("Starting DiLoCo training for worker #{state.worker_id}")
    
    # Execute DiLoCo algorithm
    result = execute_diloco_algorithm(state, training_data)
    
    {:reply, result, state}
  end

  @impl true
  def handle_call(:outer_step, _from, state) do
    # Execute one outer optimization step
    {result, new_state} = perform_outer_step(state)
    {:reply, result, new_state}
  end

  @impl true
  def handle_call({:inner_steps, data_batch}, _from, state) do
    # Execute H inner optimization steps
    {result, new_state} = perform_inner_steps(state, data_batch)
    {:reply, result, new_state}
  end

  @impl true
  def handle_call(:synchronize, _from, state) do
    # Synchronize with coalition members
    {result, new_state} = synchronize_with_coalition(state)
    {:reply, result, new_state}
  end

  @impl true
  def handle_call(:get_metrics, _from, state) do
    {:reply, state.performance_metrics, state}
  end

  @impl true
  def handle_call({:save_checkpoint, checkpoint_path}, _from, state) do
    result = save_training_checkpoint(state, checkpoint_path)
    {:reply, result, state}
  end

  @impl true
  def handle_call({:load_checkpoint, checkpoint_path}, _from, state) do
    case load_training_checkpoint(checkpoint_path) do
      {:ok, loaded_state} ->
        {:reply, :ok, loaded_state}
      error ->
        {:reply, error, state}
    end
  end

  @impl true
  def handle_info(:collect_metrics, state) do
    new_metrics = collect_performance_metrics(state)
    new_state = %{state | performance_metrics: new_metrics}
    
    schedule_metrics_collection()
    {:noreply, new_state}
  end

  @impl true
  def handle_info({:coalition_message, from_worker, message}, state) do
    case message do
      {:sync_request, barrier_id} ->
        handle_sync_request(state, from_worker, barrier_id)
        
      {:parameter_update, parameters} ->
        handle_parameter_update(state, from_worker, parameters)
        
      {:consensus_vote, vote} ->
        handle_consensus_vote(state, from_worker, vote)
        
      _ ->
        Logger.warning("Unknown coalition message: #{inspect(message)}")
        {:noreply, state}
    end
  end

  # DiLoCo Algorithm Implementation

  defp execute_diloco_algorithm(state, training_data) do
    Logger.info("Executing DiLoCo algorithm with #{state.training_config.outer_steps} outer steps")
    
    # Algorithm 1: DiLoCo Algorithm
    # Require: Initial model θ^(0)
    # Require: k workers
    # Require: Data shards {D_1, ..., D_k}
    # Require: Optimizers InnerOpt and OuterOpt
    
    initial_metrics = %{
      start_time: DateTime.utc_now(),
      outer_steps_completed: 0,
      total_inner_steps: 0,
      communication_rounds: 0
    }
    
    # Execute T outer steps
    final_state = Enum.reduce(1..state.training_config.outer_steps, state, fn outer_step, acc_state ->
      Logger.debug("Executing outer step #{outer_step}/#{state.training_config.outer_steps}")
      
      # Step 3: θ_i^(t) ← θ^(t-1) (copy global parameters to local)
      acc_state = %{acc_state | 
        local_model_state: copy_model_parameters(acc_state.global_model_state)
      }
      
      # Steps 4-9: Inner optimization loop
      {inner_result, state_after_inner} = perform_inner_optimization_loop(acc_state, training_data)
      
      # Step 12: Δ^(t) ← (1/k) ∑ᵢ (θ^(t-1) - θᵢ^(t)) (compute outer gradient)
      {outer_gradient, state_after_gradient} = compute_outer_gradient(state_after_inner)
      
      # Step 14: θ^(t) ← OuterOpt(θ^(t-1), Δ^(t)) (outer optimization)
      {_outer_result, state_after_outer} = apply_outer_optimization(state_after_gradient, outer_gradient)
      
      # Synchronize with other workers
      {_sync_result, synchronized_state} = synchronize_with_coalition(state_after_outer)
      
      # Update step counters
      %{synchronized_state | 
        step_counters: %{synchronized_state.step_counters |
          outer_step: outer_step,
          communication_rounds: synchronized_state.step_counters.communication_rounds + 1
        }
      }
    end)
    
    end_metrics = %{
      end_time: DateTime.utc_now(),
      final_loss: final_state.performance_metrics.training_loss,
      total_communication_rounds: final_state.step_counters.communication_rounds,
      wall_clock_time: DateTime.diff(DateTime.utc_now(), initial_metrics.start_time, :second)
    }
    
    {:ok, %{
      final_model: final_state.global_model_state,
      metrics: Map.merge(initial_metrics, end_metrics),
      worker_id: state.worker_id
    }}
  end

  defp perform_inner_optimization_loop(state, training_data) do
    Logger.debug("Performing #{state.training_config.inner_steps} inner optimization steps")
    
    # Steps 4-9: for inner step h = 1 ... H do
    final_state = Enum.reduce(1..state.training_config.inner_steps, state, fn inner_step, acc_state ->
      # Step 5: x ~ D_i (sample from local data shard)
      data_batch = sample_from_data_shard(acc_state.data_shard, acc_state.training_config.batch_size)
      
      # Step 6: L ← f(x, θᵢ^(t)) (compute loss)
      loss = compute_loss(acc_state.local_model_state, data_batch)
      
      # Step 7: ∇L (compute gradients)
      gradients = compute_gradients(acc_state.local_model_state, data_batch, loss)
      
      # Step 8: θᵢ^(t) ← InnerOpt(θᵢ^(t), ∇L) (inner optimization)
      {updated_params, updated_optimizer} = apply_inner_optimization(
        acc_state.local_model_state.parameters,
        gradients,
        acc_state.inner_optimizer_state
      )
      
      # Update local model state
      new_local_model = %{acc_state.local_model_state | 
        parameters: updated_params,
        metadata: %{acc_state.local_model_state.metadata |
          last_updated: DateTime.utc_now()
        }
      }
      
      # Update performance metrics
      new_metrics = %{acc_state.performance_metrics |
        training_loss: loss,
        throughput: calculate_throughput(acc_state.training_config.batch_size, DateTime.utc_now())
      }
      
      %{acc_state |
        local_model_state: new_local_model,
        inner_optimizer_state: updated_optimizer,
        performance_metrics: new_metrics,
        step_counters: %{acc_state.step_counters |
          inner_step: inner_step,
          total_steps: acc_state.step_counters.total_steps + 1
        }
      }
    end)
    
    {{:ok, :inner_steps_completed}, final_state}
  end

  defp compute_outer_gradient(state) do
    # Step 12: Δ^(t) ← (1/k) ∑ᵢ (θ^(t-1) - θᵢ^(t))
    # Compute the difference between global and local parameters
    
    outer_gradient = Map.new(state.global_model_state.parameters, fn {layer_name, global_params} ->
      local_params = Map.get(state.local_model_state.parameters, layer_name)
      gradient = subtract_tensors(global_params, local_params)
      {layer_name, gradient}
    end)
    
    Logger.debug("Computed outer gradient for #{map_size(outer_gradient)} layers")
    
    {outer_gradient, state}
  end

  defp apply_outer_optimization(state, outer_gradient) do
    # Step 14: θ^(t) ← OuterOpt(θ^(t-1), Δ^(t))
    # Apply Nesterov momentum optimization
    
    {updated_params, updated_optimizer} = case state.optimizer_config.outer_optimizer do
      :nesterov_momentum ->
        apply_nesterov_momentum(
          state.global_model_state.parameters,
          outer_gradient,
          state.outer_optimizer_state,
          state.optimizer_config
        )
        
      :sgd ->
        apply_sgd(
          state.global_model_state.parameters,
          outer_gradient,
          state.outer_optimizer_state,
          state.optimizer_config
        )
        
      :adam ->
        apply_adam(
          state.global_model_state.parameters,
          outer_gradient,
          state.outer_optimizer_state,
          state.optimizer_config
        )
    end
    
    new_global_model = %{state.global_model_state |
      parameters: updated_params,
      metadata: %{state.global_model_state.metadata |
        last_updated: DateTime.utc_now()
      }
    }
    
    new_state = %{state |
      global_model_state: new_global_model,
      outer_optimizer_state: updated_optimizer
    }
    
    {{:ok, :outer_optimization_applied}, new_state}
  end

  # Coalition Synchronization

  defp synchronize_with_coalition(state) do
    case state.coordination_service do
      nil ->
        Logger.warning("No coordination service available for synchronization")
        {{:error, :no_coordination_service}, state}
        
      coordinator_pid ->
        # Create synchronization barrier
        barrier_id = generate_barrier_id()
        
        # Send synchronization request to coordination service
        sync_message = %{
          type: :sync_request,
          worker_id: state.worker_id,
          barrier_id: barrier_id,
          model_hash: compute_model_hash(state.global_model_state),
          outer_step: state.step_counters.outer_step,
          timestamp: DateTime.utc_now()
        }
        
        case Object.CoordinationService.synchronize(coordinator_pid, sync_message) do
          {:ok, sync_result} ->
            handle_sync_result(state, sync_result)
            
          {:error, reason} ->
            Logger.error("Synchronization failed: #{inspect(reason)}")
            {{:error, reason}, state}
        end
    end
  end

  defp handle_sync_result(state, sync_result) do
    case sync_result do
      %{type: :parameters_averaged, averaged_parameters: params} ->
        # Update global model with averaged parameters
        new_global_model = %{state.global_model_state |
          parameters: params,
          metadata: %{state.global_model_state.metadata |
            last_updated: DateTime.utc_now()
          }
        }
        
        # Update communication metrics
        new_comm_state = %{state.communication_state |
          last_sync: DateTime.utc_now(),
          sync_barrier_count: state.communication_state.sync_barrier_count + 1
        }
        
        new_state = %{state |
          global_model_state: new_global_model,
          communication_state: new_comm_state
        }
        
        {{:ok, :synchronized}, new_state}
        
      %{type: :consensus_required, consensus_data: data} ->
        # Enter consensus protocol for fault tolerance
        handle_consensus_protocol(state, data)
        
      _ ->
        Logger.warning("Unknown sync result: #{inspect(sync_result)}")
        {{:error, :unknown_sync_result}, state}
    end
  end

  # Fault Tolerance and Byzantine Resistance

  defp handle_consensus_protocol(state, consensus_data) do
    # Implement Practical Byzantine Fault Tolerance (PBFT) for critical decisions
    case consensus_data.phase do
      :pre_prepare ->
        handle_pre_prepare_phase(state, consensus_data)
        
      :prepare ->
        handle_prepare_phase(state, consensus_data)
        
      :commit ->
        handle_commit_phase(state, consensus_data)
        
      _ ->
        Logger.error("Unknown consensus phase: #{consensus_data.phase}")
        {{:error, :unknown_consensus_phase}, state}
    end
  end

  defp handle_pre_prepare_phase(state, consensus_data) do
    # Validate the proposed model update
    if validate_model_update(consensus_data.proposed_update) do
      # Sign and broadcast PREPARE message
      prepare_vote = create_consensus_vote(state, :prepare, consensus_data)
      broadcast_consensus_vote(state, prepare_vote)
      
      new_consensus_state = %{state.fault_tolerance_state.consensus_state |
        view_number: consensus_data.view_number,
        votes: Map.put(state.fault_tolerance_state.consensus_state.votes, state.worker_id, prepare_vote)
      }
      
      new_ft_state = %{state.fault_tolerance_state | consensus_state: new_consensus_state}
      new_state = %{state | fault_tolerance_state: new_ft_state}
      
      {{:ok, :prepare_vote_sent}, new_state}
    else
      Logger.warning("Invalid model update in pre-prepare phase")
      {{:error, :invalid_model_update}, state}
    end
  end

  defp handle_prepare_phase(state, consensus_data) do
    # Count PREPARE votes
    prepare_votes = count_votes_by_type(state.fault_tolerance_state.consensus_state.votes, :prepare)
    required_votes = calculate_required_votes(get_coalition_size(state))
    
    if prepare_votes >= required_votes do
      # Send COMMIT vote
      commit_vote = create_consensus_vote(state, :commit, consensus_data)
      broadcast_consensus_vote(state, commit_vote)
      
      {{:ok, :commit_vote_sent}, state}
    else
      {{:ok, :waiting_for_more_prepare_votes}, state}
    end
  end

  defp handle_commit_phase(state, consensus_data) do
    # Count COMMIT votes
    commit_votes = count_votes_by_type(state.fault_tolerance_state.consensus_state.votes, :commit)
    required_votes = calculate_required_votes(get_coalition_size(state))
    
    if commit_votes >= required_votes do
      # Apply the consensus decision
      apply_consensus_decision(state, consensus_data.proposed_update)
    else
      {{:ok, :waiting_for_more_commit_votes}, state}
    end
  end

  # Optimizer Implementations

  defp apply_nesterov_momentum(parameters, gradients, optimizer_state, config) do
    # Nesterov Momentum: v_{t+1} = μv_t + ∇f(θ_t + μv_t)
    #                    θ_{t+1} = θ_t - α v_{t+1}
    
    momentum = config.momentum
    learning_rate = config.learning_rate
    
    {updated_params, updated_velocity} = Enum.reduce(parameters, {%{}, %{}}, 
      fn {layer_name, params}, {acc_params, acc_velocity} ->
        gradient = Map.get(gradients, layer_name)
        velocity = Map.get(optimizer_state.state, "velocity_#{layer_name}", zero_tensor_like(params))
        
        # Update velocity: v_{t+1} = μv_t + α∇f
        new_velocity = add_tensors(
          scale_tensor(velocity, momentum),
          scale_tensor(gradient, learning_rate)
        )
        
        # Update parameters: θ_{t+1} = θ_t - v_{t+1}
        new_params = subtract_tensors(params, new_velocity)
        
        {
          Map.put(acc_params, layer_name, new_params),
          Map.put(acc_velocity, "velocity_#{layer_name}", new_velocity)
        }
      end)
    
    new_optimizer_state = %{optimizer_state |
      state: updated_velocity,
      step_count: optimizer_state.step_count + 1
    }
    
    {updated_params, new_optimizer_state}
  end

  defp apply_adamw(parameters, gradients, optimizer_state, config) do
    # AdamW optimizer with weight decay
    learning_rate = config.learning_rate
    beta1 = config.beta1
    beta2 = config.beta2
    epsilon = config.epsilon
    weight_decay = config.weight_decay
    
    step_count = optimizer_state.step_count + 1
    
    {updated_params, updated_state} = Enum.reduce(parameters, {%{}, %{}}, 
      fn {layer_name, params}, {acc_params, acc_state} ->
        gradient = Map.get(gradients, layer_name)
        
        # Get momentum estimates
        m = Map.get(optimizer_state.state, "m_#{layer_name}", zero_tensor_like(params))
        v = Map.get(optimizer_state.state, "v_#{layer_name}", zero_tensor_like(params))
        
        # Update biased first and second moment estimates
        new_m = add_tensors(
          scale_tensor(m, beta1),
          scale_tensor(gradient, 1.0 - beta1)
        )
        
        new_v = add_tensors(
          scale_tensor(v, beta2),
          scale_tensor(elementwise_square(gradient), 1.0 - beta2)
        )
        
        # Bias correction
        m_hat = scale_tensor(new_m, 1.0 / (1.0 - :math.pow(beta1, step_count)))
        v_hat = scale_tensor(new_v, 1.0 / (1.0 - :math.pow(beta2, step_count)))
        
        # Apply weight decay (AdamW style)
        params_with_decay = scale_tensor(params, 1.0 - learning_rate * weight_decay)
        
        # Update parameters
        update = elementwise_divide(
          m_hat,
          add_tensor_scalar(elementwise_sqrt(v_hat), epsilon)
        )
        
        new_params = subtract_tensors(
          params_with_decay,
          scale_tensor(update, learning_rate)
        )
        
        {
          Map.put(acc_params, layer_name, new_params),
          Map.merge(acc_state, %{
            "m_#{layer_name}" => new_m,
            "v_#{layer_name}" => new_v
          })
        }
      end)
    
    new_optimizer_state = %{optimizer_state |
      state: updated_state,
      step_count: step_count
    }
    
    {updated_params, new_optimizer_state}
  end

  # Tensor Operations (simplified implementations)

  defp zero_tensor_like(tensor) do
    zeros = :binary.copy(<<0>>, byte_size(tensor.data))
    %{tensor | data: zeros}
  end

  defp add_tensors(tensor1, tensor2) do
    # Simplified tensor addition - in practice would use proper numerical libraries
    %{tensor1 | data: binary_add(tensor1.data, tensor2.data)}
  end

  defp subtract_tensors(tensor1, tensor2) do
    # Simplified tensor subtraction
    %{tensor1 | data: binary_subtract(tensor1.data, tensor2.data)}
  end

  defp scale_tensor(tensor, scalar) do
    # Simplified tensor scaling
    %{tensor | data: binary_scale(tensor.data, scalar)}
  end

  defp elementwise_square(tensor) do
    # Element-wise squaring
    %{tensor | data: binary_square(tensor.data)}
  end

  defp elementwise_divide(tensor1, tensor2) do
    # Element-wise division
    %{tensor1 | data: binary_divide(tensor1.data, tensor2.data)}
  end

  defp elementwise_sqrt(tensor) do
    # Element-wise square root
    %{tensor | data: binary_sqrt(tensor.data)}
  end

  defp add_tensor_scalar(tensor, scalar) do
    # Add scalar to all elements
    %{tensor | data: binary_add_scalar(tensor.data, scalar)}
  end

  # Placeholder binary operations (would be replaced with proper implementations)
  defp binary_add(data1, data2), do: data1  # Simplified
  defp binary_subtract(data1, data2), do: data1  # Simplified
  defp binary_scale(data, _scalar), do: data  # Simplified
  defp binary_square(data), do: data  # Simplified
  defp binary_divide(data1, data2), do: data1  # Simplified
  defp binary_sqrt(data), do: data  # Simplified
  defp binary_add_scalar(data, _scalar), do: data  # Simplified

  # Utility Functions

  defp init_model_state(opts) do
    %{
      parameters: Keyword.get(opts, :parameters, %{}),
      metadata: %{
        architecture: Keyword.get(opts, :architecture, "transformer"),
        layer_count: Keyword.get(opts, :layer_count, 12),
        parameter_count: Keyword.get(opts, :parameter_count, 150_000_000),
        last_updated: DateTime.utc_now()
      }
    }
  end

  defp init_optimizer_config(opts) do
    %{
      inner_optimizer: Keyword.get(opts, :inner_optimizer, :adamw),
      outer_optimizer: Keyword.get(opts, :outer_optimizer, :nesterov_momentum),
      learning_rate: Keyword.get(opts, :learning_rate, 0.001),
      momentum: Keyword.get(opts, :momentum, 0.9),
      weight_decay: Keyword.get(opts, :weight_decay, 0.01),
      beta1: Keyword.get(opts, :beta1, 0.9),
      beta2: Keyword.get(opts, :beta2, 0.999),
      epsilon: Keyword.get(opts, :epsilon, 1.0e-8)
    }
  end

  defp init_training_config(opts) do
    %{
      inner_steps: Keyword.get(opts, :inner_steps, 500),  # H parameter
      outer_steps: Keyword.get(opts, :outer_steps, 100),  # T parameter
      batch_size: Keyword.get(opts, :batch_size, 32),
      gradient_clipping: Keyword.get(opts, :gradient_clipping, 1.0),
      communication_frequency: Keyword.get(opts, :communication_frequency, 500),
      fault_tolerance_threshold: Keyword.get(opts, :fault_tolerance_threshold, 0.33),
      checkpoint_frequency: Keyword.get(opts, :checkpoint_frequency, 10)
    }
  end

  defp init_data_shard(opts) do
    %{
      shard_id: Keyword.get(opts, :shard_id, generate_shard_id()),
      data_path: Keyword.get(opts, :data_path, "/tmp/training_data"),
      total_samples: Keyword.get(opts, :total_samples, 10_000),
      current_position: 0,
      preprocessing_config: Keyword.get(opts, :preprocessing_config, %{})
    }
  end

  defp init_communication_state do
    %{
      last_sync: DateTime.utc_now(),
      pending_gradients: %{},
      sync_barrier_count: 0,
      communication_overhead: 0.0,
      bandwidth_usage: 0.0
    }
  end

  defp init_performance_metrics do
    %{
      training_loss: 0.0,
      validation_loss: 0.0,
      throughput: 0.0,
      communication_efficiency: 1.0,
      convergence_rate: 0.0,
      wall_clock_time: 0,
      compute_utilization: 0.0
    }
  end

  defp init_fault_tolerance_state do
    %{
      failed_workers: [],
      backup_checkpoints: %{},
      consensus_state: %{
        algorithm: :pbft,
        view_number: 0,
        leader: nil,
        votes: %{},
        committed_steps: 0
      },
      health_status: :healthy
    }
  end

  defp init_optimizer_state(optimizer_type) do
    %{
      type: optimizer_type,
      state: %{},
      step_count: 0,
      accumulated_gradients: %{}
    }
  end

  defp generate_object_id, do: "diloco_" <> :crypto.strong_rand_bytes(8) |> Base.encode16(case: :lower)
  defp generate_worker_id, do: "worker_" <> :crypto.strong_rand_bytes(4) |> Base.encode16(case: :lower)
  defp generate_coalition_id, do: "coalition_" <> :crypto.strong_rand_bytes(6) |> Base.encode16(case: :lower)
  defp generate_barrier_id, do: "barrier_" <> :crypto.strong_rand_bytes(4) |> Base.encode16(case: :lower)
  defp generate_shard_id, do: "shard_" <> :crypto.strong_rand_bytes(4) |> Base.encode16(case: :lower)

  # Placeholder implementations (would be properly implemented in production)
  defp sample_from_data_shard(_shard, _batch_size), do: %{data: "sample_batch"}
  defp compute_loss(_model, _batch), do: 0.5
  defp compute_gradients(_model, _batch, _loss), do: %{}
  defp apply_sgd(params, _gradients, optimizer, _config), do: {params, optimizer}
  defp apply_adam(params, _gradients, optimizer, _config), do: {params, optimizer}
  defp copy_model_parameters(model_state), do: model_state
  defp compute_model_hash(_model_state), do: :crypto.strong_rand_bytes(32)
  defp validate_model_update(_update), do: true
  defp create_consensus_vote(_state, _type, _data), do: %{type: :vote}
  defp broadcast_consensus_vote(_state, _vote), do: :ok
  defp count_votes_by_type(_votes, _type), do: 3
  defp calculate_required_votes(_coalition_size), do: 2
  defp get_coalition_size(_state), do: 4
  defp apply_consensus_decision(state, _decision), do: {{:ok, :consensus_applied}, state}
  defp calculate_throughput(_batch_size, _timestamp), do: 1000.0
  defp collect_performance_metrics(state), do: state.performance_metrics
  defp save_training_checkpoint(_state, _path), do: :ok
  defp load_training_checkpoint(_path), do: {:error, :not_implemented}
  defp schedule_metrics_collection, do: Process.send_after(self(), :collect_metrics, 5000)
  defp handle_sync_request(state, _from, _barrier_id), do: {:noreply, state}
  defp handle_parameter_update(state, _from, _params), do: {:noreply, state}
  defp handle_consensus_vote(state, _from, _vote), do: {:noreply, state}
  defp apply_inner_optimization(params, _gradients, optimizer), do: {params, optimizer}

  # Inner optimization dispatch
  defp perform_inner_steps(state, data_batch) do
    {_result, new_state} = perform_inner_optimization_loop(state, [data_batch])
    {{:ok, :inner_steps_completed}, new_state}
  end

  defp perform_outer_step(state) do
    # Compute outer gradient
    {outer_gradient, state_with_gradient} = compute_outer_gradient(state)
    
    # Apply outer optimization
    {result, final_state} = apply_outer_optimization(state_with_gradient, outer_gradient)
    
    {result, final_state}
  end
end