defmodule Automata.Reasoning.Cognitive.NeuralSymbolic.IntegrationLayer do
  @moduledoc """
  Neural-Symbolic Integration Layer

  This module serves as the main entry point for the Neural-Symbolic Integration Layer,
  coordinating the interaction between symbolic and neural representations.

  The integration layer provides bidirectional translation between symbolic and neural
  representations, verification of translations, and semantic grounding of concepts.

  Key components:
  - Translation Framework: Bidirectional conversion with confidence metrics
  - Verification Suite: Ensures correctness and consistency of translations
  - Semantic Grounding: Connects symbols to perceptual and environmental data
  """

  alias Automata.Reasoning.Cognitive.NeuralSymbolic.{
    TranslationFramework,
    VerificationSuite,
    SemanticGrounding
  }

  alias Automata.Reasoning.Cognitive.ContextualReasoning.ContextManagement.Context
  alias Automata.Reasoning.Cognitive.ContextualReasoning.ContextManagement.ContextManager

  @doc """
  Initializes the Neural-Symbolic Integration Layer with the given configuration.
  """
  @spec init(map()) :: {:ok, map()} | {:error, term()}
  def init(config \\ %{}) do
    # Initialize configuration with defaults
    config = Map.merge(default_config(), config)

    # Validate configuration
    case validate_config(config) do
      :ok ->
        {:ok, %{config: config, state: %{initialized: true}}}

      {:error, reason} ->
        {:error, reason}
    end
  end

  @doc """
  Returns the default configuration for the Neural-Symbolic Integration Layer.
  """
  @spec default_config() :: map()
  defp default_config do
    %{
      translation: %{
        default_encoder: :transformer,
        default_decoder: :rule_extraction,
        confidence_threshold: 0.7,
        vector_dimensions: 64
      },
      verification: %{
        enable_symbolic_verification: true,
        enable_neural_verification: true,
        verification_confidence_threshold: 0.8,
        perform_adversarial_testing: false
      },
      grounding: %{
        enabled_modalities: [:visual, :semantic, :abstract],
        grounding_confidence_threshold: 0.7,
        track_symbol_evolution: true
      }
    }
  end

  @doc """
  Validates the configuration.
  """
  @spec validate_config(map()) :: :ok | {:error, term()}
  defp validate_config(config) do
    # Validate translation config
    with :ok <- validate_translation_config(config.translation),
         # Validate verification config
         :ok <- validate_verification_config(config.verification),
         # Validate grounding config
         :ok <- validate_grounding_config(config.grounding) do
      :ok
    end
  end

  @doc """
  Validates the translation configuration.
  """
  @spec validate_translation_config(map()) :: :ok | {:error, term()}
  defp validate_translation_config(config) do
    cond do
      not is_number(config.confidence_threshold) or
          config.confidence_threshold < 0 or
          config.confidence_threshold > 1 ->
        {:error, "Translation confidence threshold must be between 0 and 1"}

      not is_integer(config.vector_dimensions) or
          config.vector_dimensions <= 0 ->
        {:error, "Vector dimensions must be a positive integer"}

      true ->
        :ok
    end
  end

  @doc """
  Validates the verification configuration.
  """
  @spec validate_verification_config(map()) :: :ok | {:error, term()}
  defp validate_verification_config(config) do
    cond do
      not is_boolean(config.enable_symbolic_verification) ->
        {:error, "Enable symbolic verification must be a boolean"}

      not is_boolean(config.enable_neural_verification) ->
        {:error, "Enable neural verification must be a boolean"}

      not is_number(config.verification_confidence_threshold) or
          config.verification_confidence_threshold < 0 or
          config.verification_confidence_threshold > 1 ->
        {:error, "Verification confidence threshold must be between 0 and 1"}

      not is_boolean(config.perform_adversarial_testing) ->
        {:error, "Perform adversarial testing must be a boolean"}

      true ->
        :ok
    end
  end

  @doc """
  Validates the grounding configuration.
  """
  @spec validate_grounding_config(map()) :: :ok | {:error, term()}
  defp validate_grounding_config(config) do
    valid_modalities = [:visual, :auditory, :proprioceptive, :semantic, :abstract]

    cond do
      not is_list(config.enabled_modalities) or
          Enum.any?(config.enabled_modalities, fn m -> not Enum.member?(valid_modalities, m) end) ->
        {:error, "Enabled modalities must be a list of valid modality types"}

      not is_number(config.grounding_confidence_threshold) or
          config.grounding_confidence_threshold < 0 or
          config.grounding_confidence_threshold > 1 ->
        {:error, "Grounding confidence threshold must be between 0 and 1"}

      not is_boolean(config.track_symbol_evolution) ->
        {:error, "Track symbol evolution must be a boolean"}

      true ->
        :ok
    end
  end

  @doc """
  Translates a symbolic representation to a neural representation.
  """
  @spec symbolic_to_neural(any(), keyword()) :: {:ok, map(), float()} | {:error, term()}
  def symbolic_to_neural(symbolic, options \\ []) do
    try do
      {neural, confidence} = TranslationFramework.symbolic_to_neural(symbolic, options)
      threshold = Keyword.get(options, :confidence_threshold, 0.7)

      if confidence >= threshold do
        {:ok, neural, confidence}
      else
        {:error, {:insufficient_confidence, confidence, threshold}}
      end
    rescue
      e ->
        {:error, {:translation_failed, e}}
    end
  end

  @doc """
  Translates a neural representation to a symbolic representation.
  """
  @spec neural_to_symbolic(map(), keyword()) :: {:ok, any(), float()} | {:error, term()}
  def neural_to_symbolic(neural, options \\ []) do
    try do
      {symbolic, confidence} = TranslationFramework.neural_to_symbolic(neural, options)
      threshold = Keyword.get(options, :confidence_threshold, 0.7)

      if confidence >= threshold do
        {:ok, symbolic, confidence}
      else
        {:error, {:insufficient_confidence, confidence, threshold}}
      end
    rescue
      e ->
        {:error, {:translation_failed, e}}
    end
  end

  @doc """
  Verifies a neural output using symbolic reasoning.
  """
  @spec verify_neural_output(map(), keyword()) :: {:ok, map()} | {:error, term()}
  def verify_neural_output(neural_output, options \\ []) do
    try do
      result = VerificationSuite.verify_neural_output(neural_output, options)
      threshold = Keyword.get(options, :confidence_threshold, 0.8)

      if result.confidence >= threshold do
        {:ok, result}
      else
        {:error, {:insufficient_verification_confidence, result.confidence, threshold}}
      end
    rescue
      e ->
        {:error, {:verification_failed, e}}
    end
  end

  @doc """
  Verifies symbolic reasoning using neural approaches.
  """
  @spec verify_symbolic_reasoning(any(), list(tuple()), keyword()) :: {:ok, map()} | {:error, term()}
  def verify_symbolic_reasoning(symbolic_result, reasoning_trace, options \\ []) do
    try do
      result = VerificationSuite.verify_symbolic_reasoning(symbolic_result, reasoning_trace, options)
      threshold = Keyword.get(options, :confidence_threshold, 0.8)

      if result.confidence >= threshold do
        {:ok, result}
      else
        {:error, {:insufficient_verification_confidence, result.confidence, threshold}}
      end
    rescue
      e ->
        {:error, {:verification_failed, e}}
    end
  end

  @doc """
  Verifies a reasoning process using both symbolic and neural techniques.
  """
  @spec verify_hybrid_reasoning(any(), map(), list(tuple()), keyword()) :: {:ok, map()} | {:error, term()}
  def verify_hybrid_reasoning(symbolic_result, neural_result, reasoning_trace, options \\ []) do
    try do
      result = VerificationSuite.verify_hybrid_reasoning(
        symbolic_result,
        neural_result,
        reasoning_trace,
        options
      )
      threshold = Keyword.get(options, :confidence_threshold, 0.8)

      if result.confidence >= threshold do
        {:ok, result}
      else
        {:error, {:insufficient_verification_confidence, result.confidence, threshold}}
      end
    rescue
      e ->
        {:error, {:verification_failed, e}}
    end
  end

  @doc """
  Grounds a concept across multiple sensory modalities.
  """
  @spec ground_concept(any(), list(map()), keyword()) :: {:ok, map()} | {:error, term()}
  def ground_concept(concept, sensory_data, options \\ []) do
    try do
      result = SemanticGrounding.ground_concept(concept, sensory_data, options)
      threshold = Keyword.get(options, :confidence_threshold, 0.7)

      if result.confidence >= threshold do
        {:ok, result}
      else
        {:error, {:insufficient_grounding_confidence, result.confidence, threshold}}
      end
    rescue
      e ->
        {:error, {:grounding_failed, e}}
    end
  end

  @doc """
  Identifies emergent symbols from patterns.
  """
  @spec identify_symbols(list(map()), keyword()) :: {:ok, list(map())} | {:error, term()}
  def identify_symbols(patterns, options \\ []) do
    try do
      symbols = SemanticGrounding.identify_symbols(patterns, options)
      threshold = Keyword.get(options, :confidence_threshold, 0.7)

      valid_symbols = Enum.filter(symbols, fn s -> s.confidence >= threshold end)

      if Enum.empty?(valid_symbols) and not Enum.empty?(symbols) do
        {:error, {:no_symbols_above_threshold, threshold}}
      else
        {:ok, valid_symbols}
      end
    rescue
      e ->
        {:error, {:symbol_identification_failed, e}}
    end
  end

  @doc """
  Performs a complete neural-symbolic integration process on contextual data.
  """
  @spec integrate_with_context(Context.t(), any(), list(map()), keyword()) ::
          {:ok, map()} | {:error, term()}
  def integrate_with_context(context, concept, sensory_data, options \\ []) do
    # Step 1: Ground the concept across modalities
    with {:ok, grounding} <- ground_concept(concept, sensory_data, options),
         
         # Step 2: Create symbolic and neural representations
         {:ok, symbolic, _} <- create_symbolic_representation(concept, grounding, context),
         {:ok, neural, _} <- create_neural_representation(concept, grounding),
         
         # Step 3: Verify the representations
         {:ok, symbolic_verification} <- verify_neural_output(neural, options),
         {:ok, neural_verification} <- verify_symbolic_reasoning(symbolic, [], options),
         
         # Step 4: Integrate with context
         {:ok, updated_context} <- update_context_with_concept(context, concept, symbolic, neural) do
      
      # Return the integrated result
      {:ok, %{
        concept: concept,
        grounding: grounding,
        symbolic: symbolic,
        neural: neural,
        symbolic_verification: symbolic_verification,
        neural_verification: neural_verification,
        updated_context: updated_context
      }}
    end
  end

  @doc """
  Creates a symbolic representation from grounding.
  """
  @spec create_symbolic_representation(any(), map(), Context.t()) :: {:ok, map(), float()} | {:error, term()}
  defp create_symbolic_representation(concept, grounding, context) do
    try do
      # Extract symbolic knowledge from context and grounding
      symbolic = %{
        type: :concept,
        id: concept,
        attributes: extract_attributes(grounding),
        relations: extract_relations(grounding, context)
      }

      {:ok, symbolic, grounding.confidence}
    rescue
      e ->
        {:error, {:symbolic_representation_failed, e}}
    end
  end

  @doc """
  Extracts attributes from grounding data.
  """
  @spec extract_attributes(map()) :: list(map())
  defp extract_attributes(grounding) do
    # In a real implementation, this would extract attributes from 
    # the grounding data in different modalities
    []
  end

  @doc """
  Extracts relations from grounding data and context.
  """
  @spec extract_relations(map(), Context.t()) :: list(map())
  defp extract_relations(grounding, context) do
    # In a real implementation, this would extract relations from 
    # the grounding data and existing context knowledge
    []
  end

  @doc """
  Creates a neural representation from grounding.
  """
  @spec create_neural_representation(any(), map()) :: {:ok, map(), float()} | {:error, term()}
  defp create_neural_representation(concept, grounding) do
    try do
      # In a real implementation, this would generate a unified
      # neural representation from the multimodal groundings
      neural = %{
        vector: [0.1, 0.2, 0.3, 0.4],
        metadata: %{
          source: :grounding,
          modalities: Map.keys(grounding.groundings)
        }
      }

      {:ok, neural, grounding.confidence}
    rescue
      e ->
        {:error, {:neural_representation_failed, e}}
    end
  end

  @doc """
  Updates a context with new concept knowledge.
  """
  @spec update_context_with_concept(Context.t(), any(), map(), map()) :: {:ok, Context.t()} | {:error, term()}
  defp update_context_with_concept(context, concept, symbolic, neural) do
    try do
      # In a real implementation, this would update the context with
      # the new concept knowledge in both symbolic and neural forms
      
      # For now, simply return the original context
      {:ok, context}
    rescue
      e ->
        {:error, {:context_update_failed, e}}
    end
  end

  @doc """
  Performs a complete neural-symbolic reasoning process.
  """
  @spec neural_symbolic_reasoning(Context.t(), any(), keyword()) ::
          {:ok, map()} | {:error, term()}
  def neural_symbolic_reasoning(context, query, options \\ []) do
    # Step 1: Prepare symbolic and neural query representations
    with {:ok, symbolic_query} <- prepare_symbolic_query(query, context),
         {:ok, neural_query, _} <- symbolic_to_neural(symbolic_query, options),
         
         # Step 2: Perform both symbolic and neural reasoning
         {:ok, symbolic_result, symbolic_trace} <- perform_symbolic_reasoning(symbolic_query, context),
         {:ok, neural_result} <- perform_neural_reasoning(neural_query, context),
         
         # Step 3: Translate results for cross-verification
         {:ok, neural_from_symbolic, _} <- symbolic_to_neural(symbolic_result, options),
         {:ok, symbolic_from_neural, _} <- neural_to_symbolic(neural_result, options),
         
         # Step 4: Verify results using both approaches
         {:ok, verification} <- verify_hybrid_reasoning(
           symbolic_result,
           neural_result,
           symbolic_trace,
           options
         ) do
      
      # Integrate and return the results
      integrated_result = integrate_reasoning_results(
        symbolic_result,
        neural_result,
        symbolic_from_neural,
        neural_from_symbolic,
        verification
      )
      
      {:ok, integrated_result}
    end
  end

  @doc """
  Prepares a symbolic query from the input and context.
  """
  @spec prepare_symbolic_query(any(), Context.t()) :: {:ok, map()} | {:error, term()}
  defp prepare_symbolic_query(query, context) do
    try do
      # In a real implementation, this would format the query 
      # as a proper symbolic structure using context information
      
      # For now, return a simple query structure
      {:ok, %{type: :query, content: query}}
    rescue
      e ->
        {:error, {:query_preparation_failed, e}}
    end
  end

  @doc """
  Performs symbolic reasoning on a query within a context.
  """
  @spec perform_symbolic_reasoning(map(), Context.t()) :: {:ok, any(), list(tuple())} | {:error, term()}
  defp perform_symbolic_reasoning(query, context) do
    try do
      # In a real implementation, this would use a symbolic reasoning engine
      # to process the query within the context
      
      # For now, return placeholder results
      result = %{type: :answer, content: "Symbolic answer"}
      trace = [{:init, query}, {:final, result}]
      
      {:ok, result, trace}
    rescue
      e ->
        {:error, {:symbolic_reasoning_failed, e}}
    end
  end

  @doc """
  Performs neural reasoning on a query within a context.
  """
  @spec perform_neural_reasoning(map(), Context.t()) :: {:ok, map()} | {:error, term()}
  defp perform_neural_reasoning(query, context) do
    try do
      # In a real implementation, this would use neural methods
      # to process the query within the context
      
      # For now, return placeholder results
      result = %{
        vector: [0.4, 0.3, 0.2, 0.1],
        metadata: %{confidence: 0.85}
      }
      
      {:ok, result}
    rescue
      e ->
        {:error, {:neural_reasoning_failed, e}}
    end
  end

  @doc """
  Integrates results from symbolic and neural reasoning.
  """
  @spec integrate_reasoning_results(any(), map(), any(), map(), map()) :: map()
  defp integrate_reasoning_results(symbolic_result, neural_result, symbolic_from_neural, neural_from_symbolic, verification) do
    # Determine the most reliable result based on verification
    primary_result = if verification.score > 0.8 do
      # High agreement between approaches - use symbolic for interpretability
      {:symbolic, symbolic_result}
    else
      # Choose based on individual confidence
      if neural_result.metadata.confidence > 0.9 do
        {:neural, symbolic_from_neural}
      else
        {:symbolic, symbolic_result}
      end
    end
    
    # Return the integrated result with all components
    %{
      result: elem(primary_result, 1),
      result_type: elem(primary_result, 0),
      symbolic_result: symbolic_result,
      neural_result: neural_result,
      verification: verification,
      confidence: verification.confidence
    }
  end
end