warp_engine

defmodule EnhancedADT.IsLabDBIntegration do
  @moduledoc """
  Automatic IsLabDB integration for Enhanced ADT operations.

  This module provides seamless translation from mathematical ADT operations
  to optimized IsLabDB physics commands. Domain models become intelligent
  database operations while preserving mathematical elegance.

  ## Integration Features

  - **Automatic Translation**: ADT operations become IsLabDB physics commands
  - **Physics Configuration**: ADT annotations configure quantum/gravitational behavior
  - **Wormhole Integration**: Cross-references automatically create wormhole routes
  - **Quantum Entanglement**: Related data automatically creates entanglements
  - **Performance Optimization**: Operations optimized based on physics principles

  ## Usage Pattern

  ```elixir
  use EnhancedADT.IsLabDBIntegration

  # Enhanced ADT operations automatically translate to IsLabDB
  fold user do
    User(id, name, preferences, score) ->
      # Automatically becomes: IsLabDB.cosmic_put("user:\#{id}", user, physics_context)
      store_user_with_automatic_physics(user)
  end
  ```
  """

  require Logger

  @doc """
  Enable Enhanced ADT with complete IsLabDB integration.

  This macro sets up automatic translation from mathematical ADT operations
  to optimized IsLabDB physics commands.
  """
  defmacro __using__(_opts) do
    quote do
      import EnhancedADT
      import EnhancedADT.IsLabDBIntegration.Translators
      import EnhancedADT.WormholeAnalyzer
      import EnhancedADT.QuantumAnalyzer
      import EnhancedADT.Physics

      # Enhanced ADT operations with automatic IsLabDB integration
    end
  end
end

defmodule EnhancedADT.IsLabDBIntegration.Translators do
  @moduledoc """
  Translation functions from Enhanced ADT operations to IsLabDB commands.
  """

  require Logger

  @doc """
  Translate Enhanced ADT data to IsLabDB cosmic_put operation.

  Automatically extracts physics parameters from ADT structure and annotations,
  then executes optimized IsLabDB storage with intelligent routing.
  """
  def cosmic_put_from_adt(key, %module{} = adt_data, opts \\ []) do
    # Extract physics configuration from ADT
    physics_context = extract_physics_from_adt(module, adt_data)

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

    # Analyze for automatic wormhole creation
    wormhole_analysis = analyze_wormhole_opportunities(key, adt_data, final_physics)

    # Execute IsLabDB operation with physics enhancement
    result = case IsLabDB.cosmic_put(key, adt_data, final_physics) do
      {:ok, :stored, shard_id, operation_time} ->
        # Post-storage optimizations
        post_storage_optimization(key, adt_data, shard_id, wormhole_analysis)
        {:ok, :stored, shard_id, operation_time}

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

    result
  end

  @doc """
  Translate Enhanced ADT query to IsLabDB cosmic_get operation.

  Automatically detects optimal retrieval strategy and applies quantum
  entanglement for related data pre-fetching.
  """
  def cosmic_get_for_adt(key, expected_type \\ nil, opts \\ []) do
    # Analyze retrieval context
    retrieval_context = analyze_retrieval_context(key, expected_type, opts)

    # Execute optimized retrieval
    case execute_optimized_retrieval(key, retrieval_context) do
      {:ok, value, shard_id, operation_time} ->
        # Post-retrieval enhancements
        enhanced_value = post_retrieval_enhancement(value, expected_type, retrieval_context)
        {:ok, enhanced_value, shard_id, operation_time}

      error ->
        error
    end
  end

  @doc """
  Translate Enhanced ADT batch operations to optimized IsLabDB batch commands.

  Automatically groups related operations and applies batch optimization
  strategies based on ADT structure analysis.
  """
  def batch_operations_from_adt(operations, opts \\ []) do
    # Analyze operations for batch optimization
    batch_analysis = analyze_batch_operations(operations)

    # Group operations by optimization strategy
    operation_groups = group_operations_by_strategy(operations, batch_analysis)

    # Execute grouped operations
    results = Enum.map(operation_groups, fn {strategy, ops} ->
      execute_operation_group(strategy, ops, opts)
    end)

    # Combine and return results
    flatten_batch_results(results)
  end

  # Physics Configuration Extraction

  defp extract_physics_from_adt(module, adt_data) do
    # Get physics configuration from module if it exists
    base_physics = if function_exported?(module, :__adt_physics_config__, 0) do
      module.__adt_physics_config__()
    else
      %{}
    end

    # Convert ADT field values to physics parameters
    Enum.reduce(base_physics, %{}, fn {field_name, physics_type}, acc ->
      field_value = Map.get(adt_data, field_name)
      physics_param = convert_adt_value_to_physics_param(physics_type, field_value)
      Map.put(acc, physics_type, physics_param)
    end)
  end

  defp convert_adt_value_to_physics_param(:gravitational_mass, value) when is_number(value) do
    # Normalize to reasonable gravitational mass range
    max(0.1, min(5.0, value))
  end

  defp convert_adt_value_to_physics_param(:quantum_entanglement_potential, value) when is_number(value) do
    # Normalize to 0.0-1.0 range for quantum potential
    max(0.0, min(1.0, value))
  end

  defp convert_adt_value_to_physics_param(:temporal_weight, %DateTime{} = datetime) do
    # Convert datetime to temporal weight (more recent = higher weight)
    now = DateTime.utc_now()
    seconds_diff = DateTime.diff(now, datetime)
    days_diff = seconds_diff / 86400

    # Exponential decay: recent data gets higher temporal weight
    :math.exp(-days_diff / 30.0)  # 30-day half-life
  end

  defp convert_adt_value_to_physics_param(:temporal_weight, value) when is_number(value) do
    max(0.1, min(2.0, value))
  end

  defp convert_adt_value_to_physics_param(:access_pattern, value) when value in [:hot, :warm, :cold] do
    value
  end

  defp convert_adt_value_to_physics_param(physics_type, value) do
    Logger.debug("🔬 Converting ADT field to physics param: #{physics_type} = #{inspect(value)}")
    case physics_type do
      :gravitational_mass -> 1.0
      :quantum_entanglement_potential -> 0.5
      :temporal_weight -> 1.0
      :access_pattern -> :warm
      _ -> value
    end
  end

  # Wormhole Analysis

  defp analyze_wormhole_opportunities(key, adt_data, physics_context) do
    # Analyze ADT data for cross-references that would benefit from wormholes
    cross_references = find_cross_references_in_adt(adt_data)

    # Calculate potential wormhole routes
    potential_routes = Enum.map(cross_references, fn {field_name, referenced_keys} ->
      %{
        source_key: key,
        target_keys: referenced_keys,
        field_name: field_name,
        strength: calculate_wormhole_strength(key, referenced_keys, physics_context),
        creation_priority: determine_wormhole_priority(field_name, referenced_keys)
      }
    end)

    # Filter routes that meet strength threshold
    beneficial_routes = Enum.filter(potential_routes, fn route ->
      route.strength >= 0.4 and route.creation_priority != :low
    end)

    %{
      total_cross_references: length(cross_references),
      potential_routes: potential_routes,
      beneficial_routes: beneficial_routes,
      recommendation: if(length(beneficial_routes) > 0, do: :create_wormholes, else: :skip)
    }
  end

  defp find_cross_references_in_adt(adt_data) do
    # Find fields that contain references to other data items
    Map.to_list(adt_data)
    |> Enum.filter(fn {_field, value} ->
      is_reference_like?(value)
    end)
    |> Enum.map(fn {field, value} ->
      {field, extract_reference_keys(value)}
    end)
    |> Enum.filter(fn {_field, keys} -> length(keys) > 0 end)
  end

  defp is_reference_like?(value) do
    case value do
      # String that looks like a key
      str when is_binary(str) -> String.contains?(str, ":") and String.length(str) > 5

      # List of potential references
      list when is_list(list) ->
        Enum.any?(list, &is_reference_like?/1)

      # Map with id field
      %{id: _id} -> true

      _ -> false
    end
  end

  defp extract_reference_keys(value) do
    case value do
      str when is_binary(str) ->
        if String.contains?(str, ":"), do: [str], else: []

      list when is_list(list) ->
        Enum.flat_map(list, &extract_reference_keys/1)

      %{id: id} when is_binary(id) ->
        [id]

      _ -> []
    end
  end

  defp calculate_wormhole_strength(_source_key, target_keys, physics_context) do
    base_strength = 0.5

    # Adjust based on number of references
    reference_bonus = min(0.3, length(target_keys) * 0.1)

    # Adjust based on physics context
    physics_bonus = case Map.get(physics_context, :access_pattern, :warm) do
      :hot -> 0.2
      :warm -> 0.0
      :cold -> -0.1
    end

    max(0.0, min(1.0, base_strength + reference_bonus + physics_bonus))
  end

  defp determine_wormhole_priority(field_name, target_keys) do
    field_str = Atom.to_string(field_name)

    cond do
      # High priority for common relationship fields
      field_str =~ ~r/(id|ref|relation|connect)/ -> :high
      length(target_keys) >= 3 -> :high
      length(target_keys) >= 1 -> :medium
      true -> :low
    end
  end

  # Post-Storage Optimization

  defp post_storage_optimization(key, adt_data, shard_id, wormhole_analysis) do
    # Create wormhole routes for beneficial connections
    if wormhole_analysis.recommendation == :create_wormholes do
      create_wormholes_from_analysis(key, wormhole_analysis.beneficial_routes)
    end

    # Create quantum entanglements for related data
    create_quantum_entanglements_from_adt(key, adt_data, shard_id)

    # Update access pattern analytics
    update_access_pattern_analytics(key, adt_data, shard_id)

    :ok
  end

  defp create_wormholes_from_analysis(source_key, beneficial_routes) do
    Enum.each(beneficial_routes, fn route ->
      Enum.each(route.target_keys, fn target_key ->
        case IsLabDB.WormholeRouter.establish_wormhole(source_key, target_key, route.strength) do
          {:ok, _route_id} ->
            Logger.debug("🌀 Wormhole established: #{source_key} -> #{target_key} (strength: #{route.strength})")

          {:error, reason} ->
            Logger.debug("❌ Wormhole creation failed: #{source_key} -> #{target_key} (#{reason})")
        end
      end)
    end)
  end

  defp create_quantum_entanglements_from_adt(key, adt_data, _shard_id) do
    # Find related data that should be quantum entangled
    entanglement_candidates = find_entanglement_candidates(adt_data)

    if length(entanglement_candidates) > 0 do
      case IsLabDB.create_quantum_entanglement(key, entanglement_candidates, 0.8) do
        {:ok, _entanglement_id} ->
          Logger.debug("⚛️ Quantum entanglement created: #{key} <-> #{inspect(entanglement_candidates)}")

        {:error, reason} ->
          Logger.debug("❌ Quantum entanglement failed: #{key} (#{reason})")
      end
    end
  end

  defp find_entanglement_candidates(adt_data) do
    # Find fields that should create quantum entanglements
    Map.to_list(adt_data)
    |> Enum.filter(fn {field, _value} ->
      field_str = Atom.to_string(field)
      field_str =~ ~r/(profile|setting|preference|config|metadata)/
    end)
    |> Enum.flat_map(fn {_field, value} ->
      extract_reference_keys(value)
    end)
    |> Enum.uniq()
  end

  defp update_access_pattern_analytics(_key, _adt_data, _shard_id) do
    # Update analytics for future optimization
    # This would be more sophisticated in production
    :ok
  end

  # Retrieval Optimization

  defp analyze_retrieval_context(key, expected_type, opts) do
    %{
      key: key,
      expected_type: expected_type,
      quantum_enhancement: Keyword.get(opts, :quantum_enhancement, true),
      wormhole_traversal: Keyword.get(opts, :wormhole_traversal, true),
      performance_priority: Keyword.get(opts, :performance_priority, :balanced)
    }
  end

  defp execute_optimized_retrieval(key, context) do
    # Try wormhole-optimized retrieval first if enabled
    if context.wormhole_traversal do
      case attempt_wormhole_retrieval(key) do
        {:ok, value, shard_id, operation_time} ->
          {:ok, value, shard_id, operation_time}

        {:error, :no_wormhole_route} ->
          # Fallback to standard retrieval
          fallback_to_standard_retrieval(key, context)

        error ->
          error
      end
    else
      fallback_to_standard_retrieval(key, context)
    end
  end

  defp attempt_wormhole_retrieval(key) do
    # Check if there are any wormhole routes for this key
    case IsLabDB.WormholeRouter.find_route(key, "*") do
      {:ok, route, _cost} ->
        # Use wormhole route for retrieval
        case IsLabDB.WormholeRouter.traverse_route_for_data(route) do
          {:ok, data} ->
            {:ok, data, :wormhole_route, 50}  # Assume 50μs for wormhole traversal
          error ->
            error
        end

      {:error, :no_route} ->
        {:error, :no_wormhole_route}
    end
  end

  defp fallback_to_standard_retrieval(key, context) do
    if context.quantum_enhancement do
      IsLabDB.quantum_get(key)
    else
      IsLabDB.cosmic_get(key)
    end
  end

  defp post_retrieval_enhancement(value, expected_type, _context) do
    # Enhance retrieved value if we know the expected type
    case {expected_type, value} do
      {nil, value} -> value
      {expected_module, %{} = map_data} when is_atom(expected_module) ->
        # Try to construct ADT from map data
        if function_exported?(expected_module, :new, 1) do
          try do
            expected_module.new(map_data)
          rescue
            _ -> value
          end
        else
          value
        end
      _ -> value
    end
  end

  # Batch Operations

  defp analyze_batch_operations(operations) do
    %{
      total_operations: length(operations),
      operation_types: classify_operation_types(operations),
      grouping_opportunities: find_grouping_opportunities(operations),
      estimated_performance_gain: estimate_batch_performance_gain(operations)
    }
  end

  defp classify_operation_types(operations) do
    Enum.reduce(operations, %{put: 0, get: 0, delete: 0, other: 0}, fn op, acc ->
      case op do
        {:put, _key, _value} -> Map.update!(acc, :put, &(&1 + 1))
        {:get, _key} -> Map.update!(acc, :get, &(&1 + 1))
        {:delete, _key} -> Map.update!(acc, :delete, &(&1 + 1))
        _ -> Map.update!(acc, :other, &(&1 + 1))
      end
    end)
  end

  defp find_grouping_opportunities(operations) do
    # Group operations that can be optimized together
    operations
    |> Enum.group_by(&classify_operation_for_batching/1)
    |> Enum.map(fn {group_type, ops} -> {group_type, length(ops)} end)
    |> Enum.into(%{})
  end

  defp classify_operation_for_batching({:put, key, _value}) do
    # Group by key prefix for locality
    case String.split(key, ":", parts: 2) do
      [prefix, _] -> {:put_group, prefix}
      _ -> {:put_group, :unknown}
    end
  end

  defp classify_operation_for_batching({:get, key}) do
    case String.split(key, ":", parts: 2) do
      [prefix, _] -> {:get_group, prefix}
      _ -> {:get_group, :unknown}
    end
  end

  defp classify_operation_for_batching({:delete, key}) do
    case String.split(key, ":", parts: 2) do
      [prefix, _] -> {:delete_group, prefix}
      _ -> {:delete_group, :unknown}
    end
  end

  defp classify_operation_for_batching(_), do: :other

  defp group_operations_by_strategy(operations, _analysis) do
    # Group operations by optimization strategy
    Enum.group_by(operations, &classify_operation_for_batching/1)
  end

  defp execute_operation_group({:put_group, _prefix}, operations, _opts) do
    # Execute put operations in optimized batch
    Enum.map(operations, fn {:put, key, value} ->
      cosmic_put_from_adt(key, value)
    end)
  end

  defp execute_operation_group({:get_group, _prefix}, operations, _opts) do
    # Execute get operations with quantum enhancement
    Enum.map(operations, fn {:get, key} ->
      cosmic_get_for_adt(key)
    end)
  end

  defp execute_operation_group(_group, operations, _opts) do
    # Execute ungrouped operations individually
    Enum.map(operations, &execute_single_operation/1)
  end

  defp execute_single_operation({:put, key, value}), do: cosmic_put_from_adt(key, value)
  defp execute_single_operation({:get, key}), do: cosmic_get_for_adt(key)
  defp execute_single_operation({:delete, key}), do: IsLabDB.cosmic_delete(key)
  defp execute_single_operation(op), do: {:error, {:unknown_operation, op}}

  defp estimate_batch_performance_gain(operations) do
    # Estimate performance improvement from batching
    base_gain = if length(operations) > 5, do: 0.2, else: 0.1
    grouping_gain = 0.1  # Additional gain from intelligent grouping
    max(0.0, min(0.5, base_gain + grouping_gain))
  end

  defp flatten_batch_results(grouped_results) do
    List.flatten(grouped_results)
  end
end