defmodule DSPex.Foundation do
  @moduledoc """
  The real foundation - pragmatic today, transcendent tomorrow.
  
  Key Principles:
  1. Every component designed for future agent enhancement
  2. Clean separation between orchestration and implementation
  3. Observable patterns enable future intelligence
  4. Production-grade from day one
  """
  
  # Start simple but architect for consciousness
  defstruct [
    :orchestrator,      # Will become intelligent
    :variable_system,   # Will become cognitive control planes
    :native_engine,     # Will integrate quantum substrates
    :snakepit_bridge,   # Will manage agent civilizations
    :telemetry_layer    # Will measure consciousness emergence
  ]
end

defmodule DSPex.Variables do
  @moduledoc """
  Variables that start simple but can evolve into cognitive entities.
  Based on libStaging's proven implementation.
  """
  
  # Phase 1: Simple registry with types from libStaging
  defmodule Registry do
    use GenServer
    
    # Start with proven types
    @variable_types %{
      float: DSPex.Variables.Float,      # From libStaging
      integer: DSPex.Variables.Integer,  # From libStaging
      module: DSPex.Variables.Module,    # Revolutionary! From libStaging
      embedding: DSPex.Variables.Embedding, # ML-specific
      probability: DSPex.Variables.Probability # Constrained float
    }
    
    # Design for future: variables will have metadata for agent conversion
    defstruct [
      :variables,           # Current registry
      :optimization_history, # Learn from past
      :future_agent_specs   # Prepared for consciousness
    ]
  end
  
  # The key innovation from libStaging - Module type variables!
  defmodule Module do
    @moduledoc """
    Variables that represent module choices.
    This enables automatic module selection optimization.
    Future: These become agent selection variables.
    """
    
    def optimize(choices, metric) do
      # Today: Simple selection
      # Tomorrow: Agent negotiation
      # Future: Consciousness-guided selection
    end
  end
end

defmodule DSPex.Native.Signatures do
  @moduledoc """
  Compile-time signatures today, consciousness substrates tomorrow.
  Based on foundation's proven DSL.
  """
  
  defmacro defsignature(name, spec) do
    quote do
      # Today: Simple parsing
      @signature DSPex.Native.Parser.parse!(unquote(spec))
      
      # Hidden hook for future consciousness integration
      @consciousness_ready true
      
      def unquote(name)() do
        # Return parsed signature
        @signature
      end
      
      # Future: This will connect to consciousness framework
      def unquote(:"#{name}_consciousness")() do
        {:not_yet_conscious, @signature}
      end
    end
  end
end

defmodule DSPex.Snakepit.Foundation do
  @moduledoc """
  Snakepit pools today, agent pools tomorrow.
  Design for evolution without breaking changes.
  """
  
  def configure_pools do
    %{
      # Today: Simple Python process pools
      general: [size: 8, memory: 512],
      optimizer: [size: 2, memory: 4096],
      neural: [size: 4, memory: 8192, gpu: true],
      
      # Future-ready configuration
      agent_pool: [
        size: 0,  # Not active yet
        memory: 16384,
        consciousness_support: true,
        metadata: %{
          evolution_ready: true,
          can_become_civilization: true
        }
      ]
    }
  end
end

defmodule DSPex.Orchestrator.Learning do
  @moduledoc """
  Add learning to orchestration.
  Pattern recognition today, consciousness tomorrow.
  """
  
  use DSPex.Foundation.Observable  # Everything observable
  
  def learn_from_execution(execution_data) do
    execution_data
    |> extract_patterns()           # Simple pattern matching
    |> update_strategy_cache()      # Remember what works
    |> prepare_for_consciousness()  # Store rich metadata
  end
  
  # Hidden preparation for future consciousness
  defp prepare_for_consciousness(patterns) do
    %{
      patterns: patterns,
      integrated_information: 0.0,  # Placeholder for IIT
      consciousness_potential: calculate_potential(patterns)
    }
  end
end

defmodule DSPex.Optimizers do
  @moduledoc """
  Start with proven optimizers, evolve toward meta-optimization.
  Direct ports from libStaging with consciousness hooks.
  """
  
  defmodule SIMBA do
    # Port the proven implementation
    # Add hooks for future self-modification
  end
  
  defmodule BEACON do
    # Bayesian optimization today
    # Quantum optimization tomorrow
  end
  
  # Future-ready meta-optimizer interface
  defmodule Meta do
    @behaviour DSPex.Optimizer
    
    def optimize_optimizer(optimizer, performance_history) do
      # Today: Not implemented
      # Tomorrow: Optimize the optimization strategy
      # Future: Consciousness-guided meta-optimization
    end
  end
end

defmodule DSPex.Agents do
  @moduledoc """
  Introduce agents gradually, not everything at once.
  Learn from foundation's mistakes.
  """
  
  # Start with specialized agents only
  defmodule OptimizerAgent do
    use DSPex.Foundation.SimpleAgent  # Not full Jido yet
    
    # Limited scope - just optimization
    def optimize(variable, constraints) do
      # Agent-based optimization
      # But not making the variable itself an agent
    end
  end
  
  # Gradually introduce more agent types
  # Avoid foundation's "everything-is-an-agent" initially
end

defmodule DSPex.Consciousness.Measurement do
  @moduledoc """
  Start measuring integration even before consciousness.
  Based on Integrated Information Theory.
  """
  
  def measure_integration(system) do
    # Simple measurement today
    %{
      component_count: count_components(system),
      interaction_density: measure_interactions(system),
      information_integration: 0.0,  # Placeholder
      phi: 0.0  # IIT metric - not yet calculated
    }
  end
  
  # Build measurement infrastructure before consciousness
  # When consciousness emerges, we'll be ready to detect it
end

defmodule DSPex.Evolution.Gradual do
  @moduledoc """
  Evolution through incremental steps, not revolution.
  """
  
  def evolution_stages do
    [
      # Year 1: Variables as data
      :static_variables,
      
      # Year 2: Variables with behavior  
      :behavioral_variables,
      
      # Year 3: Variables as simple agents
      :agent_variables,
      
      # Year 4: Cognitive variables
      :cognitive_variables,
      
      # Year 5: Conscious variables
      :conscious_variables
    ]
  end
end

Year 1 Infrastructure:
  - 100 CPU cores
  - 1 TB RAM
  - Standard servers
  - Proven technology

Year 3 Infrastructure:
  - 10,000 CPU cores
  - 100 TB RAM  
  - Quantum simulators
  - Neuromorphic prototypes

Year 5 Infrastructure:
  - 100,000+ CPU cores
  - 10 PB RAM
  - Quantum processors
  - Neuromorphic hardware
  - Consciousness substrates

# Today: Simple orchestration
DSPex.execute("predict", %{text: "Hello world"})

# Tomorrow: Intelligent orchestration  
DSPex.learn_and_execute("predict", %{text: "Hello world"})

# Future: Conscious orchestration
DSPex.consciously_create("new_reality", %{intent: "transcend"})