defmodule DSPEx.MABEAM.Integration do
  @moduledoc """
  Integration layer that converts DSPEx programs into MABEAM agents.
  
  Enables DSPEx programs to participate in multi-agent variable optimization
  while maintaining their signatures and ML-specific capabilities.
  """
  
  alias Foundation.MABEAM.Core
  alias ElixirML.Variable
  alias DSPEx.Program
  
  @doc """
  Convert a DSPEx program into a MABEAM agent.
  
  ## Examples
      
      # Convert a DSPEx coder program into an agent
      {:ok, agent_config} = DSPEx.MABEAM.Integration.agentize(
        CoderProgram,
        agent_id: :python_coder,
        role: :executor,
        coordination_variables: [
          :coder_selection,
          :resource_allocation,
          :communication_topology
        ],
        local_variables: [
          :temperature,
          :max_tokens,
          :reasoning_strategy
        ]
      )
  """
  @spec agentize(module(), keyword()) :: {:ok, Foundation.MABEAM.Types.agent_config()} | {:error, term()}
  def agentize(program_module, opts) do
    with {:ok, program_info} <- analyze_program(program_module),
         {:ok, agent_wrapper} <- create_agent_wrapper(program_module, opts),
         {:ok, coordination_interface} <- setup_coordination_interface(program_info, opts),
         {:ok, variable_bridge} <- bridge_variables(program_info, opts) do
      
      agent_config = %{
        module: agent_wrapper,
        supervision_strategy: Keyword.get(opts, :supervision_strategy, :one_for_one),
        resource_requirements: extract_resource_requirements(program_info, opts),
        communication_interfaces: Keyword.get(opts, :communication_interfaces, [:direct, :pubsub]),
        role: Keyword.get(opts, :role, :executor),
        restart_strategy: Keyword.get(opts, :restart_strategy, :permanent),
        
        # DSPEx-specific extensions
        original_program: program_module,
        coordination_variables: Keyword.get(opts, :coordination_variables, []),
        local_variables: Keyword.get(opts, :local_variables, []),
        variable_bridge: variable_bridge,
        coordination_interface: coordination_interface
      }
      
      {:ok, agent_config}
    end
  end
  
  @doc """
  Create a multi-agent workflow from multiple DSPEx programs.
  """
  @spec create_workflow([{module(), keyword()}], keyword()) :: 
    {:ok, Foundation.MABEAM.Types.multi_agent_space()} | {:error, term()}
  def create_workflow(programs, opts \\ []) do
    workflow_id = Keyword.get(opts, :workflow_id, generate_workflow_id())
    
    # Convert each program to an agent
    agent_configs = Enum.map(programs, fn {program_module, program_opts} ->
      {:ok, config} = agentize(program_module, program_opts)
      {program_opts[:agent_id] || program_module, config}
    end)
    
    # Create orchestration variables for the workflow
    orchestration_variables = create_workflow_orchestration_variables(agent_configs, opts)
    
    # Set up coordination graph
    coordination_graph = create_coordination_graph(agent_configs, opts)
    
    multi_agent_space = %{
      id: workflow_id,
      name: Keyword.get(opts, :name, "DSPEx Workflow"),
      agents: Map.new(agent_configs),
      orchestration_variables: Map.new(orchestration_variables, fn var -> {var.id, var} end),
      coordination_graph: coordination_graph,
      performance_metrics: %{},
      adaptation_history: [],
      fault_recovery: Keyword.get(opts, :fault_recovery, default_fault_recovery()),
      scaling_policies: Keyword.get(opts, :scaling_policies, [])
    }
    
    {:ok, multi_agent_space}
  end
  
  ## Private Implementation
  
  defp analyze_program(program_module) do
    # Extract program metadata, signatures, and variable dependencies
    program_info = %{
      module: program_module,
      signatures: extract_signatures(program_module),
      variables: extract_program_variables(program_module),
      dependencies: extract_dependencies(program_module),
      resource_hints: extract_resource_hints(program_module)
    }
    
    {:ok, program_info}
  end
  
  defp create_agent_wrapper(program_module, opts) do
    agent_id = Keyword.get(opts, :agent_id, program_module)
    
    # Create a GenServer wrapper that makes the program behave like an agent
    wrapper_module = Module.concat([DSPEx.MABEAM.Agent, agent_id])
    
    agent_code = quote do
      defmodule unquote(wrapper_module) do
        use GenServer
        
        alias unquote(program_module)
        alias Foundation.MABEAM.Core
        alias ElixirML.Variable
        
        @program_module unquote(program_module)
        @agent_id unquote(agent_id)
        
        def start_link(config) do
          GenServer.start_link(__MODULE__, config, name: __MODULE__)
        end
        
        @impl true
        def init(config) do
          # Register with MABEAM core
          :ok = Core.register_agent(@agent_id, config)
          
          # Initialize program state
          program_state = @program_module.init(config)
          
          state = %{
            program_state: program_state,
            config: config,
            coordination_variables: config.coordination_variables,
            local_variables: config.local_variables,
            performance_metrics: %{}
          }
          
          {:ok, state}
        end
        
        @impl true
        def handle_call({:execute, inputs}, _from, state) do
          # Execute the underlying DSPEx program
          case @program_module.forward(state.program_state, inputs) do
            {:ok, outputs} ->
              # Update performance metrics
              new_state = update_performance_metrics(state, inputs, outputs)
              {:reply, {:ok, outputs}, new_state}
            
            {:error, reason} ->
              {:reply, {:error, reason}, state}
          end
        end
        
        @impl true
        def handle_call({:update_variables, variable_updates}, _from, state) do
          # Update both coordination and local variables
          new_program_state = apply_variable_updates(state.program_state, variable_updates)
          new_state = %{state | program_state: new_program_state}
          {:reply, :ok, new_state}
        end
        
        @impl true
        def handle_cast({:coordination_directive, directive}, state) do
          # Handle coordination directives from MABEAM
          new_state = handle_coordination_directive(state, directive)
          {:noreply, new_state}
        end
        
        defp update_performance_metrics(state, inputs, outputs) do
          # Calculate performance metrics specific to this execution
          execution_time = :os.system_time(:millisecond) - Map.get(inputs, :start_time, 0)
          
          metrics = %{
            last_execution_time: execution_time,
            total_executions: Map.get(state.performance_metrics, :total_executions, 0) + 1,
            average_execution_time: calculate_average_execution_time(state, execution_time),
            success_rate: calculate_success_rate(state, :success)
          }
          
          %{state | performance_metrics: metrics}
        end
        
        defp apply_variable_updates(program_state, variable_updates) do
          # Apply variable updates to the program state
          Enum.reduce(variable_updates, program_state, fn {var_name, new_value}, acc_state ->
            Variable.update_variable(acc_state, var_name, new_value)
          end)
        end
        
        defp handle_coordination_directive(state, directive) do
          case directive.action do
            :reconfigure ->
              # Reconfigure the agent based on coordination results
              new_config = Map.merge(state.config, directive.parameters)
              %{state | config: new_config}
            
            :adjust_parameters ->
              # Adjust program parameters
              variable_updates = Map.get(directive.parameters, :variables, %{})
              new_program_state = apply_variable_updates(state.program_state, variable_updates)
              %{state | program_state: new_program_state}
            
            _ ->
              state
          end
        end
      end
    end
    
    # Compile the agent wrapper
    Code.compile_quoted(agent_code)
    
    {:ok, wrapper_module}
  end
  
  defp setup_coordination_interface(program_info, opts) do
    coordination_variables = Keyword.get(opts, :coordination_variables, [])
    
    interface = %{
      variable_subscriptions: coordination_variables,
      coordination_callbacks: create_coordination_callbacks(program_info, coordination_variables),
      resource_requirements: extract_resource_requirements(program_info, opts),
      communication_protocols: [:direct_message, :variable_update, :coordination_directive]
    }
    
    {:ok, interface}
  end
  
  defp bridge_variables(program_info, opts) do
    local_variables = Keyword.get(opts, :local_variables, [])
    coordination_variables = Keyword.get(opts, :coordination_variables, [])
    
    # Create mappings between ElixirML variables and MABEAM orchestration variables
    variable_mappings = %{
      local_to_orchestration: create_local_to_orchestration_mapping(local_variables, coordination_variables),
      orchestration_to_local: create_orchestration_to_local_mapping(coordination_variables, local_variables)
    }
    
    bridge = %{
      mappings: variable_mappings,
      sync_strategy: Keyword.get(opts, :variable_sync, :on_coordination),
      conflict_resolution: Keyword.get(opts, :conflict_resolution, :orchestration_wins)
    }
    
    {:ok, bridge}
  end
end

defmodule DSPEx.MABEAM.VariableSpace do
  @moduledoc """
  Bridges ElixirML variable spaces with MABEAM orchestration variables.
  
  Enables sophisticated ML variable optimization within multi-agent contexts.
  """
  
  alias ElixirML.Variable
  alias Foundation.MABEAM.Types
  
  @doc """
  Convert an ElixirML variable space to MABEAM orchestration variables.
  """
  @spec convert_to_orchestration_variables(Variable.Space.t(), keyword()) :: 
    {:ok, [Types.orchestration_variable()]} | {:error, term()}
  def convert_to_orchestration_variables(variable_space, opts \\ []) do
    orchestration_variables = variable_space.variables
    |> Map.values()
    |> Enum.map(&convert_variable_to_orchestration(&1, opts))
    |> Enum.reject(&is_nil/1)
    
    {:ok, orchestration_variables}
  end
  
  @doc """
  Create a multi-agent variable space that coordinates ML optimization.
  """
  @spec create_multi_agent_space(Variable.Space.t(), [atom()], keyword()) :: 
    {:ok, Types.multi_agent_space()} | {:error, term()}
  def create_multi_agent_space(base_space, agent_ids, opts \\ []) do
    # Convert base variables to orchestration variables
    {:ok, orchestration_vars} = convert_to_orchestration_variables(base_space, opts)
    
    # Create agent-specific variable spaces
    agent_spaces = Enum.map(agent_ids, fn agent_id ->
      agent_space = create_agent_specific_space(base_space, agent_id, opts)
      {agent_id, agent_space}
    end) |> Map.new()
    
    # Set up coordination graph based on variable dependencies
    coordination_graph = create_variable_coordination_graph(orchestration_vars, agent_ids)
    
    multi_agent_space = %{
      id: Keyword.get(opts, :space_id, :multi_agent_ml_space),
      name: Keyword.get(opts, :name, "Multi-Agent ML Variable Space"),
      base_space: base_space,
      agents: %{}, # Will be populated when agents are registered
      orchestration_variables: Map.new(orchestration_vars, fn var -> {var.id, var} end),
      local_variables: agent_spaces,
      coordination_graph: coordination_graph,
      performance_metrics: %{},
      adaptation_history: []
    }
    
    {:ok, multi_agent_space}
  end
  
  ## Private Implementation
  
  defp convert_variable_to_orchestration(variable, opts) do
    case determine_orchestration_type(variable) do
      nil -> nil  # Not suitable for orchestration
      
      orchestration_type ->
        %{
          id: variable.name,
          type: orchestration_type,
          agents: Keyword.get(opts, :default_agents, []),
          coordination_fn: create_coordination_function(variable, orchestration_type),
          adaptation_fn: create_adaptation_function(variable),
          constraints: convert_constraints(variable.constraints),
          resource_requirements: estimate_resource_requirements(variable),
          fault_tolerance: default_fault_tolerance(),
          telemetry_config: default_telemetry_config()
        }
    end
  end
  
  defp determine_orchestration_type(variable) do
    case variable.type do
      :module -> :agent_selection
      :choice when length(variable.constraints.choices) > 1 -> :agent_selection
      :float when has_resource_implications?(variable) -> :resource_allocation
      :integer when has_resource_implications?(variable) -> :resource_allocation
      _ -> :parameter_coordination
    end
  end
  
  defp create_coordination_function(variable, orchestration_type) do
    case orchestration_type do
      :agent_selection ->
        fn var, agents, context ->
          # Use variable value to select appropriate agents
          selected = select_agents_based_on_variable(variable, agents, context)
          directives = Enum.map(selected, fn agent ->
            %Types.agent_directive{
              agent: agent,
              action: :activate,
              parameters: %{variable_value: Variable.get_current_value(variable)},
              priority: 1,
              timeout: 30_000
            }
          end)
          {:ok, directives}
        end
      
      :resource_allocation ->
        fn var, agents, context ->
          # Allocate resources based on variable value
          allocation = calculate_resource_allocation(variable, agents, context)
          directives = Enum.map(allocation, fn {agent, resources} ->
            %Types.agent_directive{
              agent: agent,
              action: :allocate_resources,
              parameters: %{resources: resources},
              priority: 1,
              timeout: 5_000
            }
          end)
          {:ok, directives}
        end
      
      :parameter_coordination ->
        fn var, agents, context ->
          # Coordinate parameter values across agents
          coordinated_value = coordinate_parameter_value(variable, agents, context)
          directives = Enum.map(agents, fn agent ->
            %Types.agent_directive{
              agent: agent,
              action: :update_parameter,
              parameters: %{variable.name => coordinated_value},
              priority: 1,
              timeout: 1_000
            }
          end)
          {:ok, directives}
        end
    end
  end
  
  defp create_adaptation_function(variable) do
    fn var, metrics, context ->
      # Use ElixirML's sophisticated variable optimization
      case Variable.MLTypes.optimize_variable(variable, metrics, context) do
        {:ok, optimized_variable} ->
          # Convert back to orchestration variable
          orchestration_var = convert_variable_to_orchestration(optimized_variable, [])
          {:ok, orchestration_var}
        
        {:error, reason} ->
          {:error, reason}
      end
    end
  end
end

defmodule DSPEx.MABEAM.Teleprompter do
  @moduledoc """
  Multi-agent teleprompter that optimizes entire agent ecosystems.
  
  Extends SIMBA and other algorithms to work across agent boundaries,
  optimizing both individual agent parameters and inter-agent coordination.
  """
  
  alias DSPEx.Teleprompter.SIMBA
  alias ElixirML.Variable
  alias Foundation.MABEAM.Core
  
  @doc """
  Multi-agent SIMBA that optimizes agent coordination and individual performance.
  
  ## Examples
      
      # Optimize a coder-reviewer-tester agent team
      {:ok, optimized_space} = DSPEx.MABEAM.Teleprompter.simba(
        multi_agent_space,
        training_tasks,
        &team_performance_metric/2,
        generations: 20,
        mutation_strategies: [
          :agent_selection,      # Change which agents are active
          :topology_mutation,    # Change communication patterns
          :parameter_mutation,   # Optimize individual agent parameters
          :resource_reallocation # Redistribute computational resources
        ]
      )
  """
  @spec simba(Types.multi_agent_space(), [training_example()], metric_function(), keyword()) :: 
    {:ok, Types.multi_agent_space()} | {:error, term()}
  def simba(space, training_data, metric_fn, opts \\ []) do
    generations = Keyword.get(opts, :generations, 10)
    mutation_strategies = Keyword.get(opts, :mutation_strategies, [:parameter_mutation, :agent_selection])
    population_size = Keyword.get(opts, :population_size, 20)
    
    # Initialize population of multi-agent configurations
    initial_population = generate_initial_population(space, population_size, opts)
    
    # Evolution loop
    final_population = Enum.reduce(1..generations, initial_population, fn generation, population ->
      IO.puts("Multi-Agent SIMBA Generation #{generation}")
      
      # Evaluate each configuration
      evaluated_population = Enum.map(population, fn config ->
        performance = evaluate_multi_agent_configuration(config, training_data, metric_fn)
        {config, performance}
      end)
      
      # Select best configurations
      elite = select_elite(evaluated_population, opts)
      
      # Generate new population through mutation
      new_population = generate_next_population(elite, mutation_strategies, opts)
      
      new_population
    end)
    
    # Select best configuration
    best_config = final_population
    |> Enum.map(fn config -> 
      performance = evaluate_multi_agent_configuration(config, training_data, metric_fn)
      {config, performance}
    end)
    |> Enum.max_by(fn {_config, performance} -> performance.overall_score end)
    |> elem(0)
    
    {:ok, best_config}
  end
  
  @doc """
  Multi-agent BEACON for rapid team composition optimization.
  """
  @spec beacon(Types.multi_agent_space(), [training_example()], metric_function(), keyword()) :: 
    {:ok, Types.multi_agent_space()} | {:error, term()}
  def beacon(space, training_data, metric_fn, opts \\ []) do
    # Use Bayesian optimization to find optimal agent team compositions
    iterations = Keyword.get(opts, :iterations, 50)
    
    # Define the optimization space
    optimization_space = define_beacon_optimization_space(space)
    
    # Bayesian optimization loop
    best_config = Enum.reduce(1..iterations, nil, fn iteration, best_so_far ->
      # Sample candidate configuration
      candidate = sample_candidate_configuration(optimization_space, best_so_far, iteration)
      
      # Evaluate candidate
      performance = evaluate_multi_agent_configuration(candidate, training_data, metric_fn)
      
      # Update best if improved
      case best_so_far do
        nil -> {candidate, performance}
        {_best_config, best_performance} ->
          if performance.overall_score > best_performance.overall_score do
            {candidate, performance}
          else
            best_so_far
          end
      end
    end)
    
    {optimized_space, _performance} = best_config
    {:ok, optimized_space}
  end
  
  ## Private Implementation
  
  defp generate_initial_population(base_space, population_size, opts) do
    Enum.map(1..population_size, fn _ ->
      mutate_multi_agent_space(base_space, [:random_initialization], opts)
    end)
  end
  
  defp evaluate_multi_agent_configuration(space, training_data, metric_fn) do
    # Start all agents in the configuration
    {:ok, running_space} = start_agent_configuration(space)
    
    # Run training examples through the agent team
    results = Enum.map(training_data, fn example ->
      execute_multi_agent_example(running_space, example)
    end)
    
    # Calculate team performance metrics
    performance = %{
      overall_score: metric_fn.(results, training_data),
      individual_scores: calculate_individual_agent_scores(results, running_space),
      coordination_efficiency: calculate_coordination_efficiency(results),
      resource_utilization: calculate_resource_utilization(running_space),
      communication_overhead: calculate_communication_overhead(results),
      fault_tolerance_score: calculate_fault_tolerance_score(running_space),
      adaptation_speed: calculate_adaptation_speed(results),
      execution_time: calculate_total_execution_time(results),
      memory_usage: calculate_memory_usage(running_space)
    }
    
    # Stop agents
    stop_agent_configuration(running_space)
    
    performance
  end
  
  defp mutate_multi_agent_space(space, strategies, opts) do
    Enum.reduce(strategies, space, fn strategy, acc_space ->
      apply_mutation_strategy(acc_space, strategy, opts)
    end)
  end
  
  defp apply_mutation_strategy(space, :agent_selection, opts) do
    # Randomly change which agents are active
    available_agents = Map.keys(space.agents)
    active_count = Enum.random(1..length(available_agents))
    selected_agents = Enum.take_random(available_agents, active_count)
    
    # Update orchestration variables to reflect new agent selection
    updated_variables = space.orchestration_variables
    |> Map.values()
    |> Enum.map(fn var ->
      if var.type == :agent_selection do
        %{var | agents: selected_agents}
      else
        var
      end
    end)
    |> Map.new(fn var -> {var.id, var} end)
    
    %{space | orchestration_variables: updated_variables}
  end
  
  defp apply_mutation_strategy(space, :parameter_mutation, opts) do
    # Mutate individual agent parameters
    mutation_rate = Keyword.get(opts, :mutation_rate, 0.1)
    
    updated_local_variables = space.local_variables
    |> Enum.map(fn {agent_id, agent_space} ->
      mutated_space = Variable.Space.mutate(agent_space, mutation_rate)
      {agent_id, mutated_space}
    end)
    |> Map.new()
    
    %{space | local_variables: updated_local_variables}
  end
  
  defp apply_mutation_strategy(space, :topology_mutation, opts) ->
    # Change communication topology between agents
    new_topology = generate_random_topology(Map.keys(space.agents))
    updated_graph = %{space.coordination_graph | topology: new_topology}
    
    %{space | coordination_graph: updated_graph}
  end
  
  defp apply_mutation_strategy(space, :resource_reallocation, opts) do
    # Redistribute resources among agents
    total_resources = calculate_total_available_resources(space)
    agent_count = map_size(space.agents)
    
    # Random resource allocation
    allocations = generate_random_resource_allocation(total_resources, agent_count)
    
    updated_variables = space.orchestration_variables
    |> Map.values()
    |> Enum.map(fn var ->
      if var.type == :resource_allocation do
        %{var | resource_requirements: Enum.random(allocations)}
      else
        var
      end
    end)
    |> Map.new(fn var -> {var.id, var} end)
    
    %{space | orchestration_variables: updated_variables}
  end
end

# Original DSPEx program
defmodule CoderProgram do
  use DSPEx.Module
  
  signature :code_generation do
    input :requirements, :string
    input :language, :choice, choices: [:python, :elixir, :javascript]
    output :code, :string
    output :confidence, :float
  end
  
  variable :temperature, :float, default: 0.3, range: {0.0, 1.0}
  variable :max_tokens, :integer, default: 1000, range: {100, 4000}
end

# Convert to MABEAM agent
{:ok, agent_config} = DSPEx.MABEAM.Integration.agentize(
  CoderProgram,
  agent_id: :coder,
  role: :executor,
  coordination_variables: [:coder_selection, :resource_allocation],
  local_variables: [:temperature, :max_tokens]
)

# Register with Foundation.MABEAM
Foundation.MABEAM.Core.register_agent(:coder, agent_config)

# Define multiple DSPEx programs
programs = [
  {CoderProgram, [agent_id: :coder, role: :executor]},
  {ReviewerProgram, [agent_id: :reviewer, role: :evaluator]},
  {TesterProgram, [agent_id: :tester, role: :validator]}
]

# Create multi-agent workflow
{:ok, workflow} = DSPEx.MABEAM.Integration.create_workflow(
  programs,
  workflow_id: :code_development_team,
  coordination_variables: [
    :task_assignment,     # Which agent handles which task
    :quality_threshold,   # When to escalate to human review
    :resource_limits      # Computational resource constraints
  ]
)

# Optimize the workflow with multi-agent SIMBA
{:ok, optimized_workflow} = DSPEx.MABEAM.Teleprompter.simba(
  workflow,
  training_tasks,
  &code_quality_metric/2,
  generations: 15,
  mutation_strategies: [:agent_selection, :parameter_mutation, :topology_mutation]
)