defmodule EnhancedADT.IsLabDBIntegration.Macros do
  @moduledoc """
  Integration macros for Enhanced ADT with automatic IsLabDB translation.

  This module provides enhanced versions of Enhanced ADT macros that automatically
  integrate with IsLabDB physics operations. The macros transform mathematical
  ADT operations into intelligent database commands while preserving elegance.
  """

  @doc """
  Enhanced fold operation with automatic IsLabDB integration.

  This macro extends the base Enhanced ADT fold operation with automatic
  translation to IsLabDB physics commands and optimization.
  """
  defmacro fold(value, opts \\ [], do: clauses) do
    # Extract IsLabDB integration options
    enable_islab_integration = Keyword.get(opts, :enable_islab_integration, true)
    auto_physics_optimization = Keyword.get(opts, :auto_physics_optimization, true)
    wormhole_analysis = Keyword.get(opts, :wormhole_analysis, true)
    quantum_correlation = Keyword.get(opts, :quantum_correlation, true)

    if enable_islab_integration do
      # Use enhanced fold with IsLabDB integration
      quote do
        require Logger

        # Performance tracking for integrated operations
        integrated_fold_start = :os.system_time(:microsecond)

        # Execute base Enhanced ADT fold
        base_result = EnhancedADT.Fold.fold(unquote(value), unquote(opts), do: unquote(clauses))

        # Apply IsLabDB integration enhancements
        enhanced_result = if unquote(auto_physics_optimization) do
          EnhancedADT.IsLabDBIntegration.apply_physics_optimization(base_result, unquote(value))
        else
          base_result
        end

        # Apply wormhole analysis if enabled
        wormhole_enhanced_result = if unquote(wormhole_analysis) do
          EnhancedADT.WormholeAnalyzer.apply_fold_wormhole_analysis(enhanced_result, unquote(value))
        else
          enhanced_result
        end

        # Apply quantum correlation if enabled
        final_result = if unquote(quantum_correlation) do
          EnhancedADT.QuantumAnalyzer.apply_fold_quantum_correlation(wormhole_enhanced_result, unquote(value))
        else
          wormhole_enhanced_result
        end

        # Performance analytics
        integrated_fold_end = :os.system_time(:microsecond)
        integration_overhead = integrated_fold_end - integrated_fold_start

        if integration_overhead > 5000 do  # > 5ms
          Logger.debug("🔬 Enhanced ADT integrated fold completed in #{integration_overhead}μs")
        end

        final_result
      end
    else
      # Use base Enhanced ADT fold without integration
      quote do
        EnhancedADT.Fold.fold(unquote(value), unquote(opts), do: unquote(clauses))
      end
    end
  end

  @doc """
  Enhanced bend operation with automatic wormhole network generation.

  This macro extends the base Enhanced ADT bend operation with automatic
  wormhole network creation and IsLabDB topology optimization.
  """
  defmacro bend(opts, do: clauses) do
    # Extract IsLabDB integration options
    enable_islab_integration = Keyword.get(opts, :enable_islab_integration, true)
    auto_wormhole_creation = Keyword.get(opts, :auto_wormhole_creation, true)
    network_optimization = Keyword.get(opts, :network_optimization, true)

    if enable_islab_integration do
      # Use enhanced bend with IsLabDB integration
      quote do
        require Logger

        # Performance tracking for integrated operations
        integrated_bend_start = :os.system_time(:microsecond)

        # Execute base Enhanced ADT bend
        base_result = EnhancedADT.Bend.bend(unquote(opts), do: unquote(clauses))

        # Extract wormhole network information if bend returned metadata
        {structure_result, network_metadata} = case base_result do
          {result, metadata} -> {result, metadata}
          result -> {result, %{}}
        end

        # Apply automatic wormhole creation if enabled
        wormhole_result = if unquote(auto_wormhole_creation) and Map.has_key?(network_metadata, :wormhole_connections) do
          EnhancedADT.WormholeAnalyzer.create_automatic_wormhole_network(network_metadata.wormhole_connections)
          structure_result
        else
          structure_result
        end

        # Apply network optimization if enabled
        optimized_result = if unquote(network_optimization) and Map.has_key?(network_metadata, :network_topology) do
          EnhancedADT.WormholeAnalyzer.optimize_network_topology(network_metadata.network_topology)
          wormhole_result
        else
          wormhole_result
        end

        # Performance analytics
        integrated_bend_end = :os.system_time(:microsecond)
        integration_overhead = integrated_bend_end - integrated_bend_start

        if integration_overhead > 10000 do  # > 10ms
          Logger.debug("🌀 Enhanced ADT integrated bend completed in #{integration_overhead}μs")
        end

        # Return result with integration metadata
        if Map.keys(network_metadata) |> length() > 0 do
          {optimized_result, Map.put(network_metadata, :integration_overhead_us, integration_overhead)}
        else
          optimized_result
        end
      end
    else
      # Use base Enhanced ADT bend without integration
      quote do
        EnhancedADT.Bend.bend(unquote(opts), do: unquote(clauses))
      end
    end
  end

  @doc """
  Automatic ADT storage with physics optimization.

  This macro provides a convenient interface for storing ADT data with
  automatic physics parameter extraction and optimization.
  """
  defmacro store_adt(key, adt_data, opts \\ []) do
    quote do
      require Logger

      # Extract ADT type information for optimization
      adt_module = unquote(adt_data).__struct__

      # Apply automatic physics optimization
      physics_config = if function_exported?(adt_module, :__adt_physics_config__, 0) do
        adt_module.__adt_physics_config__()
      else
        %{}
      end

      # Merge with manual overrides
      final_physics = Map.merge(physics_config, Keyword.get(unquote(opts), :physics, %{}))

      # Store with automatic IsLabDB integration
      case EnhancedADT.IsLabDBIntegration.Translators.cosmic_put_from_adt(
        unquote(key),
        unquote(adt_data),
        Keyword.put(unquote(opts), :physics, final_physics)
      ) do
        {:ok, :stored, shard_id, operation_time} ->
          Logger.debug("📦 ADT stored: #{unquote(key)} in #{shard_id} (#{operation_time}μs)")
          {:ok, :stored, shard_id, operation_time}

        error ->
          Logger.warning("❌ ADT storage failed: #{unquote(key)} - #{inspect(error)}")
          error
      end
    end
  end

  @doc """
  Automatic ADT retrieval with physics optimization.

  This macro provides a convenient interface for retrieving ADT data with
  automatic physics optimization and type reconstruction.
  """
  defmacro retrieve_adt(key, expected_type, opts \\ []) do
    quote do
      require Logger

      # Retrieve with automatic IsLabDB integration
      case EnhancedADT.IsLabDBIntegration.Translators.cosmic_get_for_adt(
        unquote(key),
        unquote(expected_type),
        unquote(opts)
      ) do
        {:ok, value, shard_id, operation_time} ->
          Logger.debug("📦 ADT retrieved: #{unquote(key)} from #{shard_id} (#{operation_time}μs)")
          {:ok, value, shard_id, operation_time}

        error ->
          Logger.debug("❌ ADT retrieval failed: #{unquote(key)} - #{inspect(error)}")
          error
      end
    end
  end

  @doc """
  Batch ADT operations with automatic optimization.

  This macro provides optimized batch operations for multiple ADT operations
  with automatic grouping and physics optimization.
  """
  defmacro batch_adt_operations(operations, opts \\ []) do
    quote do
      require Logger

      # Execute batch operations with automatic optimization
      Logger.debug("📦 Executing batch ADT operations: #{length(unquote(operations))} operations")

      batch_start_time = :os.system_time(:microsecond)

      case EnhancedADT.IsLabDBIntegration.Translators.batch_operations_from_adt(
        unquote(operations),
        unquote(opts)
      ) do
        results when is_list(results) ->
          batch_end_time = :os.system_time(:microsecond)
          batch_duration = batch_end_time - batch_start_time

          successful = Enum.count(results, fn
            {:ok, _, _, _} -> true
            _ -> false
          end)

          Logger.debug("📦 Batch ADT operations completed: #{successful}/#{length(results)} successful in #{batch_duration}μs")
          results

        error ->
          Logger.warning("❌ Batch ADT operations failed: #{inspect(error)}")
          error
      end
    end
  end
end

defmodule EnhancedADT.IsLabDBIntegration.Optimizer do
  @moduledoc """
  Compile-time optimizer for Enhanced ADT with IsLabDB integration.

  This module provides compile-time analysis and optimization of Enhanced ADT
  definitions to generate optimal IsLabDB integration code.
  """

  defmacro __before_compile__(_env) do
    quote do
      @doc """
      Get compile-time optimization metadata for this module.

      Returns information about the optimizations applied during compilation
      and recommendations for runtime optimization.
      """
      def __adt_optimization_metadata__ do
        %{
          optimization_level: :standard,
          physics_optimizations: [],
          wormhole_optimizations: [],
          quantum_optimizations: [],
          compile_time: :os.system_time(:millisecond),
          recommendations: []
        }
      end
    end
  end
end

defmodule EnhancedADT.IsLabDBIntegration.PhysicsConfig do
  @moduledoc """
  Physics configuration helpers for Enhanced ADT integration.

  Provides utility functions for configuring physics parameters and
  optimizing Enhanced ADT operations for IsLabDB.
  """

  @doc """
  Configure optimal physics for a workload type.

  Generates physics configuration optimized for specific workload patterns.
  """
  def configure_for_workload(workload_type) do
    EnhancedADT.Physics.optimize_for_workload(workload_type)
  end

  @doc """
  Analyze ADT module and generate optimal physics configuration.

  Examines an ADT module's structure and generates recommended physics
  configuration for optimal IsLabDB performance.
  """
  def analyze_and_configure(adt_module) do
    EnhancedADT.Physics.analyze_adt_physics(adt_module)
  end

  @doc """
  Validate physics configuration for an ADT module.

  Checks physics configuration for consistency and optimization opportunities.
  """
  def validate_configuration(adt_module, physics_config) do
    # Get ADT structure info
    structure_analysis = EnhancedADT.Physics.analyze_adt_physics(adt_module)

    # Validate provided configuration
    validation_result = EnhancedADT.Physics.validate_physics_config(physics_config)

    # Compare with optimal configuration
    optimal_config = structure_analysis.final_config
    compatibility_score = calculate_compatibility_score(physics_config, optimal_config)

    %{
      structure_analysis: structure_analysis,
      validation_result: validation_result,
      compatibility_score: compatibility_score,
      recommendations: generate_configuration_recommendations(physics_config, optimal_config),
      overall_assessment: determine_configuration_assessment(validation_result, compatibility_score)
    }
  end

  # Helper Functions

  defp calculate_compatibility_score(provided_config, optimal_config) do
    # Calculate how well the provided configuration matches the optimal one
    common_keys = Map.keys(provided_config) |> Enum.filter(&Map.has_key?(optimal_config, &1))

    if length(common_keys) == 0 do
      0.0
    else
      compatibility_scores = Enum.map(common_keys, fn key ->
        provided_value = Map.get(provided_config, key)
        optimal_value = Map.get(optimal_config, key)
        calculate_value_compatibility(key, provided_value, optimal_value)
      end)

      Enum.sum(compatibility_scores) / length(compatibility_scores)
    end
  end

  defp calculate_value_compatibility(key, provided_value, optimal_value) do
    case {key, provided_value, optimal_value} do
      # Numerical values - calculate percentage similarity
      {_, pv, ov} when is_number(pv) and is_number(ov) ->
        if ov == 0, do: (if pv == 0, do: 1.0, else: 0.0), else: 1.0 - abs(pv - ov) / ov

      # Boolean values - exact match
      {_, pv, ov} when is_boolean(pv) and is_boolean(ov) ->
        if pv == ov, do: 1.0, else: 0.0

      # Atom values - exact match
      {_, pv, ov} when is_atom(pv) and is_atom(ov) ->
        if pv == ov, do: 1.0, else: 0.0

      # Different types - no compatibility
      _ -> 0.0
    end
  end

  defp generate_configuration_recommendations(provided_config, optimal_config) do
    recommendations = []

    # Check for missing optimal parameters
    missing_params = Map.keys(optimal_config) -- Map.keys(provided_config)
    recommendations = Enum.reduce(missing_params, recommendations, fn param, acc ->
      optimal_value = Map.get(optimal_config, param)
      [%{
        type: :add_parameter,
        parameter: param,
        recommended_value: optimal_value,
        reason: "Optimal configuration includes this parameter for better performance",
        priority: determine_parameter_priority(param)
      } | acc]
    end)

    # Check for suboptimal parameter values
    common_params = Map.keys(provided_config) |> Enum.filter(&Map.has_key?(optimal_config, &1))
    recommendations = Enum.reduce(common_params, recommendations, fn param, acc ->
      provided_value = Map.get(provided_config, param)
      optimal_value = Map.get(optimal_config, param)

      compatibility = calculate_value_compatibility(param, provided_value, optimal_value)

      if compatibility < 0.8 do
        [%{
          type: :adjust_parameter,
          parameter: param,
          current_value: provided_value,
          recommended_value: optimal_value,
          compatibility_score: compatibility,
          reason: "Current value differs significantly from optimal configuration",
          priority: determine_adjustment_priority(param, compatibility)
        } | acc]
      else
        acc
      end
    end)

    recommendations
  end

  defp determine_parameter_priority(param) do
    case param do
      :gravitational_mass -> :high
      :quantum_entanglement_potential -> :high
      :wormhole_creation_threshold -> :medium
      :temporal_weight -> :medium
      :entropy_optimization -> :medium
      _ -> :low
    end
  end

  defp determine_adjustment_priority(param, compatibility_score) do
    base_priority = determine_parameter_priority(param)

    # Adjust based on compatibility score
    case {base_priority, compatibility_score} do
      {:high, score} when score < 0.5 -> :critical
      {:high, _} -> :high
      {:medium, score} when score < 0.3 -> :high
      {:medium, _} -> :medium
      {:low, score} when score < 0.2 -> :medium
      _ -> :low
    end
  end

  defp determine_configuration_assessment(validation_result, compatibility_score) do
    validation_score = validation_result.validation_score

    overall_score = (validation_score + compatibility_score) / 2

    cond do
      overall_score >= 0.9 -> :excellent
      overall_score >= 0.8 -> :good
      overall_score >= 0.6 -> :acceptable
      overall_score >= 0.4 -> :needs_improvement
      true -> :poor
    end
  end
end