defmodule DSPex.Variable do
  @moduledoc """
  Variables as universal coordinators that can optimize any aspect of the system.
  """
  
  defstruct [
    :name,
    :type,           # :float, :choice, :module, :composite, :conditional
    :value,
    :constraints,
    :dependencies,   # Other variables this depends on
    :observers,      # Agents/modules watching this variable
    :optimizer,      # Current optimizer working on this
    :history,        # Optimization history
    :metadata
  ]
  
  # Any DSPy module parameter can become a variable
  def from_dspy_param(module, param_name, opts \\ []) do
    %__MODULE__{
      name: "#{module}.#{param_name}",
      type: infer_type(module, param_name),
      constraints: extract_constraints(module, param_name),
      optimizer: opts[:optimizer] || :mipro_v2
    }
  end
  
  # Variables can coordinate across system boundaries
  def coordinate(variable, change) do
    # Notify all observers
    notify_observers(variable, change)
    
    # Update dependent variables
    propagate_dependencies(variable, change)
    
    # Trigger re-optimization if needed
    maybe_reoptimize(variable, change)
  end
end

defmodule DSPex.CognitiveOrchestrator do
  @moduledoc """
  Real-time adaptation of DSPy modules based on performance feedback.
  """
  
  use GenServer
  
  defstruct [
    :modules,              # Active DSPy modules
    :performance_history,  # Performance metrics over time
    :adaptation_rules,     # When/how to adapt
    :execution_intervals,  # Dynamic execution timing
    :resource_monitors     # System resource tracking
  ]
  
  # Cognitive variables for orchestration
  @cognitive_variables [
    execution_strategy: [
      :performance_optimized,
      :resource_conserving,
      :adaptive_learning,
      :multi_agent_coordinated
    ],
    module_selection: [
      :chain_of_thought,
      :react,
      :program_of_thought,
      :auto_select  # Let the system choose
    ]
  ]
  
  def adapt_execution(state) do
    performance_trend = analyze_trend(state.performance_history)
    
    case performance_trend do
      :improving -> accelerate_execution(state)
      :degrading -> switch_strategy(state)
      :unstable -> stabilize_execution(state)
    end
  end
  
  # Auto-select best DSPy module based on task
  def select_module(task, state) do
    task_analysis = analyze_task(task)
    performance_data = get_module_performance(state)
    
    best_module = task_analysis
    |> score_modules(performance_data)
    |> select_highest_scoring()
    
    # Track selection for learning
    record_selection(task, best_module)
    
    best_module
  end
end

defmodule DSPex.Agent do
  @moduledoc """
  DSPy modules as MABEAM agents with coordination capabilities.
  """
  
  use MABEAM.Agent
  
  # Agent capabilities declaration
  capabilities do
    provides :dspy_optimization
    provides :prompt_generation
    provides :module_composition
    
    requires :llm_access
    requires :training_data
  end
  
  # Coordinate multiple DSPy modules
  def coordinate_modules(modules, task) do
    MABEAM.Coordination.consensus(modules, fn module ->
      module.evaluate_fitness_for_task(task)
    end)
  end
  
  # Distributed optimization across agents
  def distributed_optimize(program, dataset) do
    agents = discover_optimizer_agents()
    
    # Split work across agents
    work_distribution = distribute_optimization_work(dataset, agents)
    
    # Parallel optimization with coordination
    results = MABEAM.parallel_map(work_distribution, fn {agent, data} ->
      agent.optimize_subset(program, data)
    end)
    
    # Merge results with consensus
    merge_optimization_results(results)
  end
end

defmodule DSPex.Patterns do
  @moduledoc """
  Advanced DSPy patterns with cognitive orchestration.
  """
  
  # Self-correcting chain of thought with backtracking
  def self_correcting_cot(signature, input, opts \\ []) do
    max_corrections = opts[:max_corrections] || 3
    
    Stream.unfold({input, 0}, fn {current_input, attempts} ->
      if attempts >= max_corrections do
        nil
      else
        result = DSPex.chain_of_thought(signature, current_input)
        
        case validate_reasoning(result) do
          :valid -> 
            {result, nil}  # Success, stop
          {:invalid, reason} ->
            # Backtrack and try again with correction
            corrected = apply_correction(current_input, reason)
            {nil, {corrected, attempts + 1}}
        end
      end
    end)
    |> Enum.find(&(&1 != nil))
  end
  
  # Tree-of-thoughts with parallel exploration
  def tree_of_thoughts(signature, input, opts \\ []) do
    breadth = opts[:breadth] || 3
    depth = opts[:depth] || 3
    
    # Generate initial thoughts in parallel
    initial_thoughts = DSPex.parallel(
      List.duplicate({:thought, signature, input}, breadth)
    )
    
    # Explore tree with cognitive pruning
    explore_thought_tree(initial_thoughts, depth, opts)
  end
  
  # Meta-programming with self-scaffolding
  def self_scaffolding_program(task_description) do
    # Use DSPy to generate its own program
    scaffold_signature = DSPex.signature(
      "task_description -> program_code: str, test_cases: list[dict]"
    )
    
    {:ok, result} = DSPex.predict(scaffold_signature, %{
      task_description: task_description
    })
    
    # Compile and validate generated program
    compile_and_validate_program(result.program_code, result.test_cases)
  end
end

defmodule DSPex.Streaming do
  @moduledoc """
  Real-time streaming with cognitive monitoring and adaptation.
  """
  
  def stream_with_monitoring(signature, input, opts \\ []) do
    # Create monitored stream
    {:ok, stream_id} = start_monitored_stream(signature, input)
    
    Stream.resource(
      fn -> init_stream_state(stream_id) end,
      fn state ->
        case get_next_chunk(state) do
          {:chunk, text, metrics} ->
            # Monitor quality in real-time
            state = update_quality_metrics(state, metrics)
            
            # Adapt if quality degrades
            if should_adapt?(state) do
              state = adapt_streaming_strategy(state)
            end
            
            {[{:text, text}], state}
            
          {:done, final_metrics} ->
            {:halt, finalize_stream(state, final_metrics)}
        end
      end,
      fn state -> cleanup_stream(state) end
    )
  end
  
  # Parallel streaming from multiple modules
  def multi_stream(modules, input) do
    streams = Enum.map(modules, fn module ->
      Task.async(fn ->
        stream_with_monitoring(module.signature, input)
      end)
    end)
    
    # Merge streams with intelligent selection
    merge_streams_with_selection(streams)
  end
end

defmodule DSPex.Infrastructure do
  @moduledoc """
  Production infrastructure with comprehensive monitoring and fault tolerance.
  """
  
  # Health monitoring across all components
  def health_check do
    %{
      pools: check_pool_health(),
      agents: check_agent_health(),
      optimizers: check_optimizer_health(),
      resources: check_resource_usage(),
      performance: check_performance_metrics()
    }
  end
  
  # Chaos engineering support
  def chaos_test(scenario) do
    case scenario do
      :pool_failure -> simulate_pool_failures()
      :network_partition -> simulate_network_issues()
      :resource_exhaustion -> simulate_memory_pressure()
      :optimizer_divergence -> simulate_optimization_failures()
    end
  end
  
  # Multi-layer telemetry
  def setup_telemetry do
    # Pool-level metrics
    attach_pool_telemetry()
    
    # Module-level metrics
    attach_module_telemetry()
    
    # Optimization metrics
    attach_optimizer_telemetry()
    
    # Cognitive metrics
    attach_cognitive_telemetry()
  end
end

defmodule DSPex.ElixirMLIntegration do
  @moduledoc """
  Deep integration with ElixirML's unified architecture.
  """
  
  # Use ElixirML's schema engine
  def validate_with_schema(data, schema) do
    ElixirML.Schema.validate(data, schema)
  end
  
  # Integrate with ExDantic for Pydantic compatibility
  def from_pydantic_model(model) do
    ExDantic.to_dspex_signature(model)
  end
  
  # Use ElixirML's variable system
  def create_optimizable_module(module, variable_config) do
    variables = ElixirML.Variables.from_config(variable_config)
    
    %DSPex.OptimizableModule{
      module: module,
      variables: variables,
      optimizer: select_optimizer(variables)
    }
  end
  
  # Leverage MABEAM for distributed execution
  def distributed_execution(program, dataset) do
    MABEAM.distribute(program, dataset, 
      strategy: :capability_based,
      coordination: :consensus
    )
  end
end