defmodule DSPex.Variables.Registry do
  use GenServer
  
  @type variable_id :: String.t()
  @type variable_type :: :float | :integer | :choice | :module | :embedding
  
  defstruct [:variables, :observers, :optimizers, :history]
  
  def start_link(opts) do
    GenServer.start_link(__MODULE__, opts, name: __MODULE__)
  end
  
  def register(name, type, initial_value, opts \\ []) do
    GenServer.call(__MODULE__, {:register, name, type, initial_value, opts})
  end
  
  def update(variable_id, new_value, metadata \\ %{}) do
    GenServer.call(__MODULE__, {:update, variable_id, new_value, metadata})
  end
  
  def observe(variable_id, observer_pid) do
    GenServer.cast(__MODULE__, {:observe, variable_id, observer_pid})
  end
end

# snakepit/priv/python/dspex_variables.py
class VariableAdapter:
    """Adds variable awareness to DSPy modules"""
    
    def __init__(self, elixir_bridge):
        self.bridge = elixir_bridge
        self.variable_cache = {}
    
    def inject_variables(self, module, variable_mappings):
        """Inject variable values into DSPy module parameters"""
        for param_path, variable_id in variable_mappings.items():
            value = self._get_variable_value(variable_id)
            self._set_nested_attr(module, param_path, value)
        return module
    
    def wrap_module(self, module_class, variable_specs):
        """Create variable-aware wrapper for DSPy module"""
        class VariableAwareModule(module_class):
            def __init__(self, *args, **kwargs):
                super().__init__(*args, **kwargs)
                self._variable_specs = variable_specs
                self._variable_adapter = VariableAdapter(elixir_bridge)
            
            def forward(self, *args, **kwargs):
                # Apply current variable values
                self._variable_adapter.inject_variables(self, self._variable_specs)
                # Track variable usage
                self._track_variable_usage()
                # Execute original forward
                result = super().forward(*args, **kwargs)
                # Report variable impact
                self._report_variable_impact(result)
                return result
        
        return VariableAwareModule

defmodule DSPex.Variables.DSPyBridge do
  @moduledoc """
  Bridge between Elixir variables and Python DSPy modules
  """
  
  def create_variable_aware_module(module_type, signature, variable_specs) do
    # Create module with variable injection points
    {:ok, module_id} = Snakepit.Python.call(:runtime, """
    from dspex_variables import VariableAdapter
    import dspy
    
    # Get module class
    module_class = getattr(dspy, '#{module_type}')
    
    # Create adapter
    adapter = VariableAdapter(bridge)
    
    # Create wrapped class
    VariableAwareClass = adapter.wrap_module(module_class, #{inspect(variable_specs)})
    
    # Instantiate
    module = VariableAwareClass('#{signature}')
    
    module
    """, store_as: ID.generate("variable_aware_module"))
    
    {:ok, module_id}
  end
  
  def execute_with_variables(module_id, inputs, variable_values) do
    # Update variables in registry
    Enum.each(variable_values, fn {var_id, value} ->
      DSPex.Variables.Registry.update(var_id, value)
    end)
    
    # Execute module
    {:ok, result} = Snakepit.Python.call(:runtime, """
    stored.#{module_id}(**#{inspect(inputs)})
    """)
    
    # Extract variable feedback
    feedback = extract_variable_feedback(result)
    
    {:ok, result, feedback}
  end
end

defmodule DSPex.Native.Evaluation do
  @moduledoc """
  Native evaluation engine with variable impact tracking
  """
  
  defstruct [:metrics, :variable_impacts, :traces]
  
  def evaluate_with_variables(program, dataset, variables, opts \\ []) do
    results = dataset
    |> Task.async_stream(fn example ->
      evaluate_example(program, example, variables, opts)
    end, max_concurrency: opts[:max_concurrency] || 4)
    |> Enum.map(fn {:ok, result} -> result end)
    
    %__MODULE__{
      metrics: aggregate_metrics(results),
      variable_impacts: calculate_variable_impacts(results, variables),
      traces: if(opts[:collect_traces], do: extract_traces(results), else: nil)
    }
  end
  
  defp evaluate_example(program, example, variables, opts) do
    # Start trace
    trace = DSPex.Trace.start()
    
    # Apply variables
    configured_program = apply_variables(program, variables)
    
    # Execute with trace
    {result, trace} = DSPex.Trace.capture(trace, fn ->
      execute_program(configured_program, example)
    end)
    
    # Calculate metrics
    metrics = calculate_metrics(result, example, opts[:metrics] || [:accuracy])
    
    # Extract variable attribution
    variable_attribution = attribute_to_variables(trace, variables)
    
    %{
      example: example,
      result: result,
      metrics: metrics,
      trace: trace,
      variable_attribution: variable_attribution
    }
  end
  
  defp calculate_variable_impacts(results, variables) do
    # Aggregate impact of each variable on metrics
    Enum.reduce(results, %{}, fn result, acc ->
      Enum.reduce(result.variable_attribution, acc, fn {var_id, impact}, acc2 ->
        Map.update(acc2, var_id, [impact], &[impact | &1])
      end)
    end)
    |> Enum.map(fn {var_id, impacts} ->
      {var_id, %{
        mean_impact: Statistics.mean(impacts),
        std_dev: Statistics.standard_deviation(impacts),
        samples: length(impacts)
      }}
    end)
    |> Map.new()
  end
end

defmodule DSPex.Variables.Types do
  @moduledoc """
  Native variable type system with consciousness metadata
  """
  
  defmodule Type do
    @callback validate(value :: any()) :: {:ok, any()} | {:error, String.t()}
    @callback cast(value :: any()) :: {:ok, any()} | {:error, String.t()}
    @callback constraints() :: keyword()
    @callback consciousness_metadata() :: map()
  end
  
  defmodule Float do
    @behaviour Type
    
    def validate(value) when is_float(value), do: {:ok, value}
    def validate(value) when is_integer(value), do: {:ok, value * 1.0}
    def validate(_), do: {:error, "must be a float"}
    
    def cast(value) when is_binary(value) do
      case Float.parse(value) do
        {float, ""} -> {:ok, float}
        _ -> {:error, "cannot cast to float"}
      end
    end
    def cast(value), do: validate(value)
    
    def constraints, do: [min: :float, max: :float, step: :float]
    
    def consciousness_metadata do
      %{
        integration_potential: 0.7,
        can_become_agent: false,
        evolution_stage: :static,
        phi_contribution: 0.1,
        description: "Continuous parameter for fine-tuning"
      }
    end
  end
  
  defmodule Module do
    @behaviour Type
    
    def validate(value) when is_atom(value), do: {:ok, value}
    def validate(value) when is_binary(value), do: {:ok, String.to_atom(value)}
    def validate(_), do: {:error, "must be a module identifier"}
    
    def cast(value), do: validate(value)
    
    def constraints, do: [choices: :list]
    
    def consciousness_metadata do
      %{
        integration_potential: 0.95,
        can_become_agent: true,
        evolution_stage: :intelligent,
        phi_contribution: 0.8,
        description: "Module selection enables cognitive orchestration"
      }
    end
  end
end

defmodule DSPex.Trace do
  @moduledoc """
  Execution trace system with variable attribution
  """
  
  defstruct [
    :id,
    :start_time,
    :events,
    :variable_uses,
    :decision_points,
    :module_calls
  ]
  
  def start do
    %__MODULE__{
      id: ID.generate("trace"),
      start_time: System.monotonic_time(),
      events: [],
      variable_uses: %{},
      decision_points: [],
      module_calls: []
    }
  end
  
  def capture(trace, fun) do
    Process.put(:dspex_trace, trace)
    result = fun.()
    final_trace = Process.get(:dspex_trace)
    Process.delete(:dspex_trace)
    {result, final_trace}
  end
  
  def record_variable_use(variable_id, value, context) do
    case Process.get(:dspex_trace) do
      %__MODULE__{} = trace ->
        event = %{
          type: :variable_use,
          variable_id: variable_id,
          value: value,
          context: context,
          timestamp: System.monotonic_time()
        }
        
        updated_trace = %{trace |
          events: [event | trace.events],
          variable_uses: Map.update(trace.variable_uses, variable_id, [event], &[event | &1])
        }
        
        Process.put(:dspex_trace, updated_trace)
        
      nil ->
        # No active trace
        :ok
    end
  end
  
  def record_decision(decision_type, chosen, alternatives, metadata \\ %{}) do
    case Process.get(:dspex_trace) do
      %__MODULE__{} = trace ->
        decision = %{
          type: decision_type,
          chosen: chosen,
          alternatives: alternatives,
          metadata: metadata,
          timestamp: System.monotonic_time()
        }
        
        updated_trace = %{trace |
          events: [{:decision, decision} | trace.events],
          decision_points: [decision | trace.decision_points]
        }
        
        Process.put(:dspex_trace, updated_trace)
        
      nil ->
        :ok
    end
  end
end

defmodule DSPex.Native.Optimizers.VariableOptimizer do
  @moduledoc """
  Base optimizer for variable-aware optimization
  """
  
  defstruct [:strategy, :metrics, :constraints, :history]
  
  def optimize(program, dataset, variables, opts \\ []) do
    strategy = opts[:strategy] || DSPex.Native.Optimizers.SimulatedAnnealing
    
    initial_state = %{
      variables: variables,
      values: get_initial_values(variables),
      best_score: -:infinity,
      best_values: nil,
      iteration: 0
    }
    
    final_state = Enum.reduce_while(1..opts[:max_iterations], initial_state, fn i, state ->
      # Evaluate current configuration
      evaluation = DSPex.Native.Evaluation.evaluate_with_variables(
        program,
        dataset,
        apply_values(state.variables, state.values),
        opts
      )
      
      score = calculate_score(evaluation, opts[:metrics])
      
      # Update best if improved
      state = if score > state.best_score do
        %{state | best_score: score, best_values: state.values}
      else
        state
      end
      
      # Generate next values using strategy
      next_values = strategy.next_values(
        state.values,
        evaluation.variable_impacts,
        temperature(i, opts[:max_iterations])
      )
      
      # Check convergence
      if converged?(state, opts) do
        {:halt, state}
      else
        {:cont, %{state | values: next_values, iteration: i}}
      end
    end)
    
    {:ok, final_state.best_values, final_state.best_score}
  end
  
  defp temperature(iteration, max_iterations) do
    # Simulated annealing temperature schedule
    initial_temp = 1.0
    final_temp = 0.01
    
    initial_temp * :math.pow(final_temp / initial_temp, iteration / max_iterations)
  end
end

defmodule DSPex.Native.Optimizers.SIMBA_GV do
  @moduledoc """
  SIMBA adapted for generalized variable optimization
  """
  
  alias DSPex.Native.{Evaluation, Variables}
  
  def optimize(modules, dataset, variables, opts \\ []) do
    # Phase 1: Sample for variable coverage
    samples = sample_for_variables(dataset, variables, opts)
    
    # Phase 2: Initialize variable values
    initial_values = initialize_variables(modules, samples, variables)
    
    # Phase 3: Mutate and bootstrap
    bootstrapped = bootstrap_with_mutations(
      modules,
      samples,
      variables,
      initial_values,
      opts
    )
    
    # Phase 4: Amplify successful patterns
    amplified = amplify_patterns(bootstrapped, modules, dataset, variables)
    
    {:ok, amplified}
  end
  
  defp sample_for_variables(dataset, variables, opts) do
    # Smart sampling that ensures variable space coverage
    module_vars = Enum.filter(variables, &(&1.type == :module))
    continuous_vars = Enum.filter(variables, &(&1.type in [:float, :integer]))
    
    # For module variables, ensure we sample data that works well with each option
    module_samples = if length(module_vars) > 0 do
      Enum.flat_map(module_vars, fn var ->
        Enum.flat_map(var.constraints.choices, fn choice ->
          sample_for_module_choice(dataset, var, choice, opts)
        end)
      end)
    else
      []
    end
    
    # For continuous variables, sample across the range
    continuous_samples = if length(continuous_vars) > 0 do
      sample_across_ranges(dataset, continuous_vars, opts)
    else
      []
    end
    
    Enum.uniq(module_samples ++ continuous_samples)
  end
  
  defp bootstrap_with_mutations(modules, samples, variables, initial_values, opts) do
    # Generate mutations of variable values
    mutations = generate_mutations(initial_values, variables, opts[:mutation_count] || 10)
    
    # Evaluate each mutation across all modules
    evaluated_mutations = Enum.map(mutations, fn mutation ->
      scores = Enum.map(modules, fn module ->
        configured = apply_variables(module, mutation)
        evaluation = Evaluation.evaluate_with_variables(
          configured,
          samples,
          variables,
          opts
        )
        {module.id, evaluation.metrics}
      end)
      
      {mutation, aggregate_cross_module_score(scores)}
    end)
    
    # Select best mutations for bootstrapping
    evaluated_mutations
    |> Enum.sort_by(fn {_, score} -> score end, :desc)
    |> Enum.take(opts[:bootstrap_count] || 5)
    |> Enum.map(fn {mutation, _} -> mutation end)
  end
  
  defp amplify_patterns(bootstrapped_values, modules, dataset, variables) do
    # Find patterns in successful configurations
    patterns = extract_variable_patterns(bootstrapped_values, variables)
    
    # Generate new configurations based on patterns
    amplified = Enum.flat_map(patterns, fn pattern ->
      generate_from_pattern(pattern, variables, 5)
    end)
    
    # Final evaluation and selection
    all_configs = bootstrapped_values ++ amplified
    
    evaluated = Enum.map(all_configs, fn config ->
      score = evaluate_configuration(config, modules, dataset, variables)
      {config, score}
    end)
    
    evaluated
    |> Enum.sort_by(fn {_, score} -> score end, :desc)
    |> List.first()
    |> elem(0)
  end
end

reasoning_var = Variable.create(:reasoning_approach,
  type: :module,
  choices: [Predict, ChainOfThought, ReAct, ProgramOfThought],
  metadata: %{affects: "reasoning style and depth"}
)

# System automatically selects best reasoning approach

temperature_var = Variable.create(:temperature,
  type: :float,
  range: {0.0, 2.0},
  shared_by: [:ideation_module, :refinement_module, :critique_module]
)

# One temperature optimized for entire pipeline coherence

%{
  integration_potential: 0.95,  # How well it integrates
  can_become_agent: true,       # Can evolve to make decisions
  evolution_stage: :intelligent, # Current evolution level
  phi_contribution: 0.8         # Consciousness contribution
}