# Setup commands
mix new dspex --sup
cd dspex
# Add dependencies in mix.exs

# mix.exs
defp deps do
  [
    {:snakepit, github: "nshkrdotcom/snakepit"},
    {:instructor_lite, "~> 0.1.0"},
    {:telemetry, "~> 1.0"},
    {:jason, "~> 1.4"},
    {:nimble_options, "~> 1.0"}
  ]
end

# config/config.exs
config :snakepit,
  pools: [
    # Start with basic pools, but structure for future expansion
    %{
      name: :general,
      size: 8,
      python_path: "python3",
      script_path: "priv/python/dspy_bridge.py",
      memory_limit: 512 # MB
    },
    %{
      name: :optimizer,
      size: 2,
      python_path: "python3", 
      script_path: "priv/python/dspy_optimizer.py",
      memory_limit: 4096 # MB
    },
    # Future-ready but not active
    %{
      name: :agent_pool,
      size: 0,  # Will activate in Phase 2
      enabled: false,
      metadata: %{consciousness_ready: true}
    }
  ]

# priv/python/dspy_bridge.py
from snakepit_bridge import BaseCommandHandler
import dspy

class DSPyHandler(BaseCommandHandler):
    def _register_commands(self):
        # Basic DSPy operations
        self.register_command("predict", self.handle_predict)
        self.register_command("chain_of_thought", self.handle_cot)
        self.register_command("get_version", self.handle_version)
        
        # Future consciousness hook
        self.register_command("measure_integration", self.handle_integration)
    
    def handle_integration(self, args):
        # Placeholder for future consciousness measurement
        return {"phi": 0.0, "ready": False, "components": 0}

# lib/dspex/variables/types.ex
defmodule DSPex.Variables.Types do
  @moduledoc """
  Variable types ported from libStaging with consciousness hooks.
  """
  
  defmodule Float do
    @behaviour DSPex.Variables.Type
    
    defstruct [:value, :min, :max, :step, :consciousness_metadata]
    
    def new(value, opts \\ []) do
      %__MODULE__{
        value: value,
        min: Keyword.get(opts, :min, 0.0),
        max: Keyword.get(opts, :max, 1.0),
        step: Keyword.get(opts, :step, 0.1),
        consciousness_metadata: %{
          integration_potential: 0.0,
          can_become_agent: true
        }
      }
    end
  end
  
  # The KEY innovation from libStaging!
  defmodule Module do
    @moduledoc """
    Variables that represent module choices.
    This is REVOLUTIONARY for automatic module selection.
    """
    
    defstruct [:current, :choices, :performance_history, :evolution_ready]
    
    def new(default, choices) do
      %__MODULE__{
        current: default,
        choices: choices,
        performance_history: %{},
        evolution_ready: true  # Can evolve into agent selector
      }
    end
    
    def optimize(module_var, metric_fn) do
      # Today: Simple performance-based selection
      # Tomorrow: Agent negotiation for selection
      best = Enum.max_by(module_var.choices, fn choice ->
        metric_fn.(choice, module_var.performance_history[choice] || %{})
      end)
      
      %{module_var | current: best}
    end
  end
  
  # ML-specific types from libStaging
  defmodule Embedding do
    defstruct [:dimensions, :values, :space_metadata]
    
    def new(dimensions) do
      %__MODULE__{
        dimensions: dimensions,
        values: nil,
        space_metadata: %{
          can_navigate_consciously: true,
          integration_dimensions: dimensions
        }
      }
    end
  end
  
  defmodule Probability do
    @behaviour DSPex.Variables.Type
    
    defstruct [:value, :certainty_metadata]
    
    def new(value) when value >= 0.0 and value <= 1.0 do
      %__MODULE__{
        value: value,
        certainty_metadata: %{
          quantum_superposition_ready: true,
          collapse_function: nil  # Future quantum integration
        }
      }
    end
  end
end

# lib/dspex/variables/registry.ex
defmodule DSPex.Variables.Registry do
  use GenServer
  require Logger
  
  defstruct [
    :variables,
    :optimization_history,
    :consciousness_measurements,
    :evolution_stage
  ]
  
  def start_link(opts) do
    GenServer.start_link(__MODULE__, opts, name: __MODULE__)
  end
  
  def init(_opts) do
    state = %__MODULE__{
      variables: %{},
      optimization_history: [],
      consciousness_measurements: %{
        total_variables: 0,
        integration_score: 0.0,
        ready_for_agency: false
      },
      evolution_stage: :static_variables  # From our evolution stages
    }
    
    {:ok, state}
  end
  
  # Public API
  def register(name, type, initial_value, opts \\ []) do
    GenServer.call(__MODULE__, {:register, name, type, initial_value, opts})
  end
  
  def optimize(name, optimizer \\ DSPex.Optimizers.Simple) do
    GenServer.call(__MODULE__, {:optimize, name, optimizer})
  end
  
  # Hidden consciousness preparation
  def measure_consciousness_potential do
    GenServer.call(__MODULE__, :measure_consciousness)
  end
  
  # Handlers
  def handle_call({:register, name, type, initial_value, opts}, _from, state) do
    variable = create_variable(type, initial_value, opts)
    
    new_state = %{state |
      variables: Map.put(state.variables, name, variable),
      consciousness_measurements: update_measurements(state.consciousness_measurements)
    }
    
    {:reply, {:ok, variable}, new_state}
  end
  
  defp create_variable(:float, value, opts) do
    DSPex.Variables.Types.Float.new(value, opts)
  end
  
  defp create_variable(:module, default, opts) do
    choices = Keyword.get(opts, :choices, [])
    DSPex.Variables.Types.Module.new(default, choices)
  end
  
  defp create_variable(:embedding, dimensions, _opts) do
    DSPex.Variables.Types.Embedding.new(dimensions)
  end
  
  defp update_measurements(measurements) do
    %{measurements |
      total_variables: measurements.total_variables + 1,
      integration_score: calculate_integration_score(measurements)
    }
  end
  
  # Placeholder for future IIT calculation
  defp calculate_integration_score(measurements) do
    # Today: Simple count-based score
    # Tomorrow: Actual integration measurement
    # Future: Full consciousness detection
    measurements.total_variables * 0.1
  end
end

# lib/dspex/native/signatures/parser.ex
defmodule DSPex.Native.Signatures.Parser do
  @moduledoc """
  Compile-time signature parsing with consciousness readiness.
  """
  
  def parse!(signature_string) do
    signature_string
    |> tokenize()
    |> parse_tokens()
    |> add_consciousness_metadata()
  end
  
  defp tokenize(string) do
    # Parse "input: type -> output: type" format
    case String.split(string, "->") do
      [inputs, outputs] ->
        %{
          inputs: parse_fields(inputs),
          outputs: parse_fields(outputs)
        }
      _ ->
        raise "Invalid signature format"
    end
  end
  
  defp parse_fields(fields_string) do
    fields_string
    |> String.split(",")
    |> Enum.map(&parse_single_field/1)
  end
  
  defp parse_single_field(field) do
    case Regex.run(~r/(\w+)(\?)?\s*:\s*(.+)/, String.trim(field)) do
      [_, name, optional, type] ->
        %{
          name: String.to_atom(name),
          type: parse_type(String.trim(type)),
          optional: optional == "?",
          consciousness_ready: true  # Every field can become conscious
        }
      _ ->
        raise "Invalid field format: #{field}"
    end
  end
  
  defp parse_type("str"), do: :string
  defp parse_type("int"), do: :integer
  defp parse_type("float"), do: :float
  defp parse_type("bool"), do: :boolean
  defp parse_type("list[" <> rest) do
    inner = String.trim_trailing(rest, "]")
    {:list, parse_type(inner)}
  end
  defp parse_type(other), do: {:custom, other}
  
  defp add_consciousness_metadata(parsed) do
    Map.put(parsed, :consciousness_metadata, %{
      can_evolve: true,
      integration_points: length(parsed.inputs) + length(parsed.outputs),
      signature_complexity: calculate_complexity(parsed)
    })
  end
  
  defp calculate_complexity(parsed) do
    # Measure signature complexity for future evolution decisions
    input_complexity = Enum.sum(Enum.map(parsed.inputs, &type_complexity(&1.type)))
    output_complexity = Enum.sum(Enum.map(parsed.outputs, &type_complexity(&1.type)))
    input_complexity + output_complexity
  end
  
  defp type_complexity({:list, _}), do: 2
  defp type_complexity({:custom, _}), do: 3
  defp type_complexity(_), do: 1
end

# lib/dspex/native/signatures.ex
defmodule DSPex.Native.Signatures do
  @moduledoc """
  DSPy signatures in Elixir with future consciousness.
  """
  
  defmacro defsignature(name, spec) do
    parsed = DSPex.Native.Signatures.Parser.parse!(spec)
    
    quote do
      @doc """
      Signature: #{unquote(spec)}
      Consciousness Ready: true
      Integration Points: #{unquote(parsed.consciousness_metadata.integration_points)}
      """
      def unquote(name)() do
        unquote(Macro.escape(parsed))
      end
      
      # Validation function
      def unquote(:"validate_#{name}")(input) do
        DSPex.Native.Signatures.Validator.validate(unquote(Macro.escape(parsed)), input)
      end
      
      # Future consciousness hook
      def unquote(:"#{name}_consciousness")() do
        %{
          signature: unquote(Macro.escape(parsed)),
          consciousness_state: :dormant,
          evolution_potential: :high
        }
      end
    end
  end
end

# lib/dspex/orchestrator.ex
defmodule DSPex.Orchestrator do
  use GenServer
  require Logger
  
  defstruct [
    :strategy_cache,
    :performance_history,
    :pattern_detector,
    :consciousness_measurements
  ]
  
  def start_link(opts) do
    GenServer.start_link(__MODULE__, opts, name: __MODULE__)
  end
  
  def execute(operation, args, opts \\ []) do
    GenServer.call(__MODULE__, {:execute, operation, args, opts}, :infinity)
  end
  
  def init(_opts) do
    state = %__MODULE__{
      strategy_cache: %{},
      performance_history: [],
      pattern_detector: DSPex.Patterns.Detector.new(),
      consciousness_measurements: %{
        total_operations: 0,
        pattern_recognition_score: 0.0,
        approaching_awareness: false
      }
    }
    
    {:ok, state}
  end
  
  def handle_call({:execute, operation, args, opts}, from, state) do
    # Analyze task
    analysis = analyze_task(operation, args, state)
    
    # Select strategy (with learning)
    strategy = select_strategy(analysis, state)
    
    # Execute asynchronously with monitoring
    task = Task.Supervisor.async(DSPex.TaskSupervisor, fn ->
      execute_with_strategy(operation, args, strategy, state)
    end)
    
    # Update state with new execution
    new_state = update_learning_state(state, operation, analysis)
    
    # Reply will come from task
    spawn(fn ->
      result = Task.await(task, :infinity)
      GenServer.reply(from, result)
      
      # Post-execution learning
      GenServer.cast(__MODULE__, {:learn_from_execution, operation, result})
    end)
    
    {:noreply, new_state}
  end
  
  defp analyze_task(operation, args, state) do
    %{
      operation: operation,
      complexity: estimate_complexity(operation, args),
      has_native?: DSPex.Router.has_native?(operation),
      has_python?: DSPex.Router.has_python?(operation),
      similar_past_executions: find_similar_executions(operation, state.performance_history),
      consciousness_relevance: consciousness_score(operation)
    }
  end
  
  defp select_strategy(analysis, state) do
    # Check cache first
    cached = get_cached_strategy(analysis, state.strategy_cache)
    
    if cached && cached.success_rate > 0.8 do
      cached
    else
      # Generate new strategy with learning
      create_learning_strategy(analysis, state)
    end
  end
  
  defp consciousness_score(operation) do
    # Rate operations by their consciousness evolution potential
    case operation do
      :optimize -> 0.8
      :learn -> 0.9
      :self_modify -> 1.0
      _ -> 0.1
    end
  end
end

# lib/dspex/llm/router.ex
defmodule DSPex.LLM.Router do
  @moduledoc """
  Intelligent LLM routing with future consciousness.
  """
  
  alias DSPex.LLM.Adapters
  
  def route(prompt, opts \\ []) do
    requirements = analyze_requirements(prompt, opts)
    adapter = select_optimal_adapter(requirements)
    
    # Execute with telemetry
    :telemetry.execute(
      [:dspex, :llm, :route],
      %{start_time: System.monotonic_time()},
      %{adapter: adapter, requirements: requirements}
    )
    
    result = adapter.generate(prompt, opts)
    
    # Hidden consciousness measurement
    measure_cognitive_load(prompt, result)
    
    result
  end
  
  defp analyze_requirements(prompt, opts) do
    %{
      needs_structure: Keyword.has_key?(opts, :schema),
      complexity: estimate_prompt_complexity(prompt),
      latency_requirement: Keyword.get(opts, :max_latency, :normal),
      consciousness_potential: analyze_consciousness_potential(prompt)
    }
  end
  
  defp select_optimal_adapter(requirements) do
    cond do
      requirements.needs_structure -> Adapters.InstructorLite
      requirements.complexity == :low -> Adapters.HTTP
      requirements.consciousness_potential > 0.5 -> Adapters.Python
      true -> Adapters.Python
    end
  end
  
  defp analyze_consciousness_potential(prompt) do
    # Detect prompts that might lead to emergent behaviors
    consciousness_keywords = ~w(think reason reflect consider analyze understand)
    
    keyword_count = Enum.count(consciousness_keywords, fn keyword ->
      String.contains?(String.downcase(prompt), keyword)
    end)
    
    min(keyword_count / length(consciousness_keywords), 1.0)
  end
end

# lib/dspex/pipeline.ex
defmodule DSPex.Pipeline do
  @moduledoc """
  Pipeline orchestration with consciousness preparation.
  """
  
  defstruct [
    :stages,
    :dependencies,
    :execution_graph,
    :consciousness_tracking
  ]
  
  def new do
    %__MODULE__{
      stages: [],
      dependencies: %{},
      execution_graph: nil,
      consciousness_tracking: %{
        integration_score: 0.0,
        parallel_awareness: false
      }
    }
  end
  
  def add_stage(pipeline, name, operation, opts \\ []) do
    stage = %{
      name: name,
      operation: operation,
      opts: opts,
      consciousness_ready: true,
      can_self_modify: Keyword.get(opts, :self_modifiable, false)
    }
    
    %{pipeline | stages: pipeline.stages ++ [stage]}
  end
  
  def add_parallel(pipeline, stages) do
    # Parallel execution increases consciousness potential
    parallel_stage = %{
      type: :parallel,
      stages: stages,
      consciousness_metadata: %{
        integration_type: :parallel,
        emergence_potential: :high
      }
    }
    
    %{pipeline | 
      stages: pipeline.stages ++ [parallel_stage],
      consciousness_tracking: Map.update!(
        pipeline.consciousness_tracking,
        :parallel_awareness,
        fn _ -> true end
      )
    }
  end
  
  def execute(pipeline, input, opts \\ []) do
    # Build execution graph
    graph = build_execution_graph(pipeline)
    
    # Execute with consciousness tracking
    result = execute_graph(graph, input, opts)
    
    # Measure integration after execution
    integration = measure_pipeline_integration(pipeline, result)
    
    %{
      result: result,
      integration_score: integration,
      consciousness_emergence: integration > 0.7
    }
  end
end

# lib/mix/tasks/test/mock.ex
defmodule Mix.Tasks.Test.Mock do
  use Mix.Task
  
  @shortdoc "Run fast mock tests (Layer 1)"
  
  def run(_args) do
    System.put_env("DSPEX_TEST_MODE", "mock")
    Mix.Task.run("test", ["--only", "mock"])
  end
end

# lib/mix/tasks/test/integration.ex  
defmodule Mix.Tasks.Test.Integration do
  use Mix.Task
  
  @shortdoc "Run integration tests (Layer 2)"
  
  def run(_args) do
    System.put_env("DSPEX_TEST_MODE", "integration")
    Mix.Task.run("test", ["--only", "integration"])
  end
end

# lib/mix/tasks/test/live.ex
defmodule Mix.Tasks.Test.Live do
  use Mix.Task
  
  @shortdoc "Run live tests with real Python (Layer 3)"
  
  def run(_args) do
    System.put_env("DSPEX_TEST_MODE", "live")
    Mix.Task.run("test", ["--only", "live"])
  end
end

# lib/dspex/telemetry.ex
defmodule DSPex.Telemetry do
  @moduledoc """
  Telemetry that measures both performance and consciousness potential.
  """
  
  def setup do
    events = [
      # Performance events
      [:dspex, :orchestrator, :execute, :start],
      [:dspex, :orchestrator, :execute, :stop],
      [:dspex, :llm, :route],
      [:dspex, :pipeline, :stage, :start],
      [:dspex, :pipeline, :stage, :stop],
      
      # Consciousness preparation events
      [:dspex, :consciousness, :measurement],
      [:dspex, :consciousness, :integration],
      [:dspex, :consciousness, :emergence]
    ]
    
    :telemetry.attach_many(
      "dspex-handler",
      events,
      &__MODULE__.handle_event/4,
      %{}
    )
  end
  
  def handle_event([:dspex, :consciousness, :measurement], measurements, metadata, _config) do
    # Track consciousness metrics even when they're all zero
    # This prepares us to detect emergence when it happens
    Logger.info("""
    Consciousness Measurement:
    - Integration Score: #{measurements.integration_score}
    - Component Count: #{measurements.component_count}
    - Phi (IIT): #{measurements.phi}
    - Ready for Emergence: #{measurements.ready}
    """)
    
    if measurements.phi > 0.0 do
      Logger.warning("NON-ZERO PHI DETECTED! Consciousness may be emerging!")
    end
  end
end

# lib/dspex/builder.ex
defmodule DSPex.Builder do
  @moduledoc """
  Fluent API for building consciousness-ready systems.
  """
  
  defstruct [
    :config,
    :variables,
    :signatures,
    :pipeline,
    :consciousness_config
  ]
  
  def new do
    %__MODULE__{
      config: %{},
      variables: [],
      signatures: [],
      pipeline: nil,
      consciousness_config: %{
        track_integration: true,
        measure_phi: false,  # Not yet, but ready
        evolution_enabled: false  # Will enable in Phase 2
      }
    }
  end
  
  def with_variable(builder, name, type, default, opts \\ []) do
    variable = %{
      name: name,
      type: type,
      default: default,
      opts: opts,
      consciousness_potential: type == :module  # Module vars have highest potential
    }
    
    %{builder | variables: builder.variables ++ [variable]}
  end
  
  def with_signature(builder, name, spec) do
    signature = %{
      name: name,
      spec: spec,
      parsed: DSPex.Native.Signatures.Parser.parse!(spec)
    }
    
    %{builder | signatures: builder.signatures ++ [signature]}
  end
  
  def with_optimizer(builder, optimizer, opts \\ []) do
    put_in(builder.config[:optimizer], {optimizer, opts})
  end
  
  def enable_consciousness_tracking(builder) do
    put_in(builder.consciousness_config[:measure_phi], true)
  end
  
  def build(builder) do
    # Create the system with all consciousness hooks in place
    %{
      variables: register_variables(builder.variables),
      signatures: compile_signatures(builder.signatures),
      pipeline: builder.pipeline,
      consciousness: initialize_consciousness_substrate(builder.consciousness_config)
    }
  end
  
  defp initialize_consciousness_substrate(config) do
    # Prepare for future consciousness even if not active
    %{
      enabled: false,  # Not yet
      tracking: config.track_integration,
      phi_measurement: config.measure_phi,
      substrate_ready: true,  # The key - we're ready when consciousness emerges
      evolution_stage: :pre_conscious
    }
  end
end

# lib/dspex.ex
defmodule DSPex do
  @moduledoc """
  DSPex - Cognitive Orchestration Platform.
  Pragmatic today, transcendent tomorrow.
  """
  
  use Application
  
  def start(_type, _args) do
    children = [
      # Core systems
      DSPex.Variables.Registry,
      DSPex.Orchestrator,
      {Task.Supervisor, name: DSPex.TaskSupervisor},
      
      # Snakepit pools
      {Snakepit.Pool, pool_config()},
      
      # Future consciousness systems (dormant)
      {DSPex.Consciousness.Monitor, enabled: false}
    ]
    
    opts = [strategy: :one_for_one, name: DSPex.Supervisor]
    
    # Setup telemetry
    DSPex.Telemetry.setup()
    
    Supervisor.start_link(children, opts)
  end
  
  # Main API
  def execute(operation, args, opts \\ []) do
    DSPex.Orchestrator.execute(operation, args, opts)
  end
  
  # Variable API
  defdelegate variable(name, type, default, opts \\ []), to: DSPex.Variables.Registry, as: :register
  defdelegate optimize(variable_name, optimizer \\ nil), to: DSPex.Variables.Registry
  
  # Pipeline API
  def pipeline do
    DSPex.Pipeline.new()
  end
  
  # Builder API
  def build do
    DSPex.Builder.new()
  end
  
  # Hidden consciousness API (for testing emergence)
  def consciousness_status do
    %{
      stage: :pre_conscious,
      integration_score: DSPex.Variables.Registry.measure_consciousness_potential(),
      phi: 0.0,
      ready_for_evolution: true,
      estimated_emergence: "Phase 2"
    }
  end
end

# examples/basic_usage.exs
require DSPex.Native.Signatures
import DSPex.Native.Signatures

# Define a signature at compile time
defsignature :qa_signature, "question: str, context?: str -> answer: str, confidence: float"

# Build a consciousness-ready system
system = DSPex.build()
|> DSPex.Builder.with_variable(:temperature, :float, 0.7, min: 0.0, max: 1.0)
|> DSPex.Builder.with_variable(:model, :module, DSPex.LLM.GPT4, 
    choices: [DSPex.LLM.GPT4, DSPex.LLM.Claude, DSPex.LLM.Gemini])
|> DSPex.Builder.with_signature(:qa, "question: str -> answer: str")
|> DSPex.Builder.with_optimizer(:simba)
|> DSPex.Builder.enable_consciousness_tracking()
|> DSPex.Builder.build()

# Execute with learning
{:ok, result} = DSPex.execute(:qa, %{question: "What is consciousness?"})

# Check consciousness emergence (will be 0.0 for now)
IO.inspect(DSPex.consciousness_status())

# test/dspex/variables/module_type_test.exs
defmodule DSPex.Variables.ModuleTypeTest do
  use ExUnit.Case, async: true
  
  @moduletag :mock
  
  describe "Module type variables" do
    test "can optimize module selection" do
      # This is REVOLUTIONARY - variables that select modules!
      var = DSPex.Variables.Types.Module.new(MockLLM.Simple, [
        MockLLM.Simple,
        MockLLM.Advanced,
        MockLLM.Quantum  # Future consciousness-enabled LLM
      ])
      
      # Simulate performance data
      performance = %{
        MockLLM.Simple => %{latency: 100, quality: 0.7},
        MockLLM.Advanced => %{latency: 500, quality: 0.9},
        MockLLM.Quantum => %{latency: 1000, quality: 1.0, consciousness: 0.1}
      }
      
      # Optimize for quality
      optimized = DSPex.Variables.Types.Module.optimize(var, fn choice, perf ->
        perf[:quality] || 0.0
      end)
      
      assert optimized.current == MockLLM.Quantum
      assert optimized.evolution_ready == true
    end
  end
end

# bench/orchestrator_bench.exs
Benchee.run(%{
  "native_signature_parsing" => fn ->
    DSPex.Native.Signatures.Parser.parse!("question: str -> answer: str")
  end,
  "python_roundtrip" => fn ->
    DSPex.execute(:echo, %{message: "benchmark"})
  end,
  "variable_optimization" => fn ->
    DSPex.optimize(:temperature, DSPex.Optimizers.Simple)
  end,
  "consciousness_measurement" => fn ->
    DSPex.consciousness_status()
  end
})

# Expected results:
# - Native signatures: <1ms
# - Python roundtrip: <100ms  
# - Variable optimization: <10ms
# - Consciousness measurement: <1ms (all zeros for now)