defmodule Object.SystemOrchestrator do
  @moduledoc """
  Self-organizing system orchestrator that manages the entire Object ecosystem.
  
  This module enables:
  1. Dynamic system topology discovery and adaptation
  2. Emergent hierarchy formation based on capabilities
  3. Load balancing and resource optimization
  4. Fault tolerance and recovery mechanisms
  5. Performance monitoring and adaptive scaling
  
  The orchestrator itself is an Object that can reason about and modify
  the system architecture using LLM-powered decision making.
  """
  
  use GenServer
  
  alias Object.LLMIntegration
  
  defstruct [
    :orchestrator_object,
    :system_topology,
    :performance_metrics,
    :adaptation_history,
    :self_organization_rules
  ]
  
  # Client API
  
  @doc """
  Starts the system orchestrator with an Object-based approach.
  """
  def start_link(opts \\ []) do
    GenServer.start_link(__MODULE__, opts, name: __MODULE__)
  end
  
  @doc """
  Registers a new object in the system and evaluates its role.
  """
  def register_object(object) do
    GenServer.call(__MODULE__, {:register_object, object})
  end
  
  @doc """
  Triggers system self-organization based on current state.
  """
  def self_organize(trigger_reason \\ :periodic) do
    GenServer.call(__MODULE__, {:self_organize, trigger_reason}, 30_000)
  end
  
  @doc """
  Gets the current system topology and health metrics.
  """
  def get_system_status do
    GenServer.call(__MODULE__, :get_system_status)
  end
  
  # Server callbacks
  
  @impl true
  def init(_opts) do
    # Create the orchestrator as an Object with special capabilities
    orchestrator_object = Object.create_subtype(:coordinator_object, [
      id: "system_orchestrator",
      state: %{
        role: :system_orchestrator,
        authority_level: :global,
        managed_objects: %{},
        optimization_goals: [:performance, :resilience, :efficiency]
      },
      methods: [
        :analyze_system_topology,
        :optimize_object_placement,
        :coordinate_load_balancing,
        :handle_failures,
        :adapt_system_structure
      ],
      goal: &system_optimization_goal/1,
      parameters: %{
        rebalance_threshold: 0.8,
        failure_recovery_timeout: 30_000,
        self_organization_interval: 300_000  # 5 minutes
      }
    ])
    
    state = %__MODULE__{
      orchestrator_object: orchestrator_object,
      system_topology: initialize_topology(),
      performance_metrics: initialize_metrics(),
      adaptation_history: [],
      self_organization_rules: initialize_self_organization_rules()
    }
    
    # Schedule periodic self-organization
    schedule_self_organization()
    
    {:ok, state}
  end
  
  @impl true
  def handle_call({:register_object, object}, _from, state) do
    # Use LLM integration to analyze the object's role in the system
    analysis_result = analyze_object_role(state.orchestrator_object, object)
    
    case analysis_result do
      {:ok, role_analysis, updated_orchestrator} ->
        # Update system topology
        updated_topology = integrate_object_into_topology(
          state.system_topology, 
          object, 
          role_analysis
        )
        
        # Update managed objects
        updated_managed = Map.put(
          updated_orchestrator.state.managed_objects,
          object.id,
          %{object: object, role: role_analysis.suggested_role, integrated_at: DateTime.utc_now()}
        )
        
        final_orchestrator = Object.update_state(updated_orchestrator, %{
          managed_objects: updated_managed
        })
        
        updated_state = %{state |
          orchestrator_object: final_orchestrator,
          system_topology: updated_topology
        }
        
        {:reply, {:ok, role_analysis}, updated_state}
        
      {:error, reason} ->
        {:reply, {:error, reason}, state}
    end
  end
  
  @impl true
  def handle_call({:self_organize, trigger_reason}, _from, state) do
    # Use the orchestrator object's LLM capabilities to reason about system optimization
    optimization_task = %{
      type: :system_self_organization,
      trigger: trigger_reason,
      current_topology: state.system_topology,
      performance_metrics: state.performance_metrics,
      constraints: extract_system_constraints(state)
    }
    
    case LLMIntegration.reason_about_goal(
      state.orchestrator_object,
      "Optimize the system architecture for better performance, resilience, and efficiency",
      optimization_task
    ) do
      {:ok, reasoning_result, updated_orchestrator} ->
        # Execute the optimization plan
        optimization_result = execute_self_organization_plan(
          state,
          reasoning_result.action_plan
        )
        
        # Record adaptation in history
        adaptation_record = %{
          timestamp: DateTime.utc_now(),
          trigger: trigger_reason,
          reasoning: reasoning_result,
          changes_made: optimization_result.changes,
          success: optimization_result.success
        }
        
        updated_state = %{state |
          orchestrator_object: updated_orchestrator,
          system_topology: optimization_result.new_topology,
          adaptation_history: [adaptation_record | state.adaptation_history]
        }
        
        {:reply, {:ok, optimization_result}, updated_state}
        
      {:error, reason} ->
        {:reply, {:error, reason}, state}
    end
  end
  
  @impl true
  def handle_call(:get_system_status, _from, state) do
    status = %{
      orchestrator_health: :healthy,
      managed_objects_count: map_size(state.orchestrator_object.state.managed_objects),
      topology: summarize_topology(state.system_topology),
      performance_metrics: state.performance_metrics,
      last_adaptation: List.first(state.adaptation_history)
    }
    
    {:reply, status, state}
  end
  
  @impl true
  def handle_info(:self_organize, state) do
    # Trigger periodic self-organization
    GenServer.cast(self(), {:self_organize, :periodic})
    schedule_self_organization()
    {:noreply, state}
  end
  
  # Private functions
  
  defp system_optimization_goal(state) do
    # Multi-objective optimization function
    performance_score = calculate_performance_score(state)
    resilience_score = calculate_resilience_score(state)
    efficiency_score = calculate_efficiency_score(state)
    
    # Weighted combination
    0.4 * performance_score + 0.3 * resilience_score + 0.3 * efficiency_score
  end
  
  defp analyze_object_role(orchestrator_object, new_object) do
    # Use LLM integration to analyze how this object fits into the system
    analysis_prompt = %{
      new_object_capabilities: new_object.methods,
      new_object_type: new_object.subtype,
      new_object_state: summarize_object_state(new_object),
      current_system_objects: orchestrator_object.state.managed_objects,
      system_needs: identify_system_gaps(orchestrator_object)
    }
    
    case LLMIntegration.reason_about_goal(
      orchestrator_object,
      "Determine the optimal role and integration strategy for this new object",
      analysis_prompt
    ) do
      {:ok, reasoning_result, updated_orchestrator} ->
        role_analysis = %{
          suggested_role: extract_role_from_reasoning(reasoning_result),
          integration_strategy: extract_integration_strategy(reasoning_result),
          expected_impact: reasoning_result.success_probability,
          coordination_requirements: extract_coordination_needs(reasoning_result)
        }
        
        {:ok, role_analysis, updated_orchestrator}
        
      error ->
        error
    end
  end
  
  defp integrate_object_into_topology(topology, object, role_analysis) do
    # Add object to appropriate layer based on its analyzed role
    layer = determine_topology_layer(role_analysis.suggested_role)
    
    updated_layer = Map.get(topology.layers, layer, [])
                   |> Kernel.++([%{object: object, role: role_analysis.suggested_role}])
    
    updated_layers = Map.put(topology.layers, layer, updated_layer)
    
    %{topology |
      layers: updated_layers,
      connections: update_topology_connections(topology.connections, object, role_analysis)
    }
  end
  
  defp execute_self_organization_plan(state, action_plan) do
    changes = []
    
    # Execute each action in the plan
    final_topology = Enum.reduce(action_plan, state.system_topology, fn action, acc_topology ->
      case action.action do
        "rebalance_load" ->
          perform_load_rebalancing(acc_topology, action)
        
        "reorganize_hierarchy" ->
          reorganize_object_hierarchy(acc_topology, action)
        
        "optimize_connections" ->
          optimize_object_connections(acc_topology, action)
        
        "scale_capacity" ->
          scale_system_capacity(acc_topology, action)
        
        _ ->
          acc_topology
      end
    end)
    
    %{
      success: true,
      new_topology: final_topology,
      changes: changes
    }
  end
  
  defp initialize_topology do
    %{
      layers: %{
        presentation: [],      # Human interfaces, dashboards
        coordination: [],      # Orchestrators, coordinators
        processing: [],        # AI agents, processors
        data: [],             # Sensors, databases
        infrastructure: []     # Actuators, system services
      },
      connections: %{},
      metrics: %{
        total_objects: 0,
        active_connections: 0,
        average_latency: 0.0
      }
    }
  end
  
  defp initialize_metrics do
    %{
      throughput: 0.0,
      response_time: 0.0,
      error_rate: 0.0,
      resource_utilization: 0.0,
      adaptation_frequency: 0.0
    }
  end
  
  defp initialize_self_organization_rules do
    [
      %{
        condition: "performance_degradation",
        threshold: 0.2,
        action: :rebalance_load,
        priority: :high
      },
      %{
        condition: "object_failure",
        threshold: 1,
        action: :redistribute_tasks,
        priority: :critical
      },
      %{
        condition: "capacity_exceeded",
        threshold: 0.9,
        action: :scale_out,
        priority: :medium
      }
    ]
  end
  
  defp schedule_self_organization do
    Process.send_after(self(), :self_organize, 300_000)  # 5 minutes
  end
  
  # Simplified helper functions
  defp calculate_performance_score(_state), do: 0.8
  defp calculate_resilience_score(_state), do: 0.7
  defp calculate_efficiency_score(_state), do: 0.9
  defp summarize_object_state(object), do: Map.take(object.state, [:role, :status, :capacity])
  defp identify_system_gaps(_orchestrator), do: ["load_balancing", "fault_tolerance"]
  defp extract_role_from_reasoning(_reasoning), do: :processing_agent
  defp extract_integration_strategy(_reasoning), do: :gradual_integration
  defp extract_coordination_needs(_reasoning), do: [:peer_communication, :status_reporting]
  defp determine_topology_layer(:coordinator_object), do: :coordination
  defp determine_topology_layer(:ai_agent), do: :processing
  defp determine_topology_layer(:sensor_object), do: :data
  defp determine_topology_layer(_), do: :processing
  defp update_topology_connections(connections, _object, _role), do: connections
  defp perform_load_rebalancing(topology, _action), do: topology
  defp reorganize_object_hierarchy(topology, _action), do: topology
  defp optimize_object_connections(topology, _action), do: topology
  defp scale_system_capacity(topology, _action), do: topology
  defp extract_system_constraints(_state), do: %{max_objects: 1000, max_latency: 100}
  defp summarize_topology(topology), do: Map.take(topology, [:metrics])
end