defmodule Automata.Reasoning.Cognitive.NeuralSymbolic.TranslationFramework do
  @moduledoc """
  Bidirectional Translation Framework for Neural-Symbolic Integration

  This module provides a bidirectional translation mechanism between neural representations
  (high-dimensional continuous vectors) and symbolic representations (discrete structures
  like rules, facts, and logical expressions).

  Key features:
  - Modular encoders/decoders with standardized interfaces
  - Progressive refinement of translations with feedback loops
  - Confidence metrics for translation quality
  """

  alias Automata.Reasoning.Cognitive.NeuralSymbolic.TranslationFramework.{
    Encoder,
    Decoder,
    Metrics
  }

  defmodule Encoder do
    @moduledoc """
    Encoder module for converting symbolic representations to neural representations.
    """

    @type symbolic_input :: map() | list() | tuple() | atom() | String.t()
    @type neural_output :: %{
            vector: list(float()),
            metadata: map()
          }
    @type encoder_options :: [
            model: atom(),
            dimensions: pos_integer(),
            normalization: :none | :l1 | :l2 | :minmax
          ]

    @doc """
    Encodes a symbolic representation into a neural representation.

    Options:
    - model: The encoding model to use (:transformer, :embedding, :semantic, etc.)
    - dimensions: The dimensionality of the output vector
    - normalization: The normalization strategy for the output vector
    """
    @spec encode(symbolic_input(), encoder_options()) :: {neural_output(), float()}
    def encode(symbolic_input, options \\ []) do
      model = Keyword.get(options, :model, :default)
      dimensions = Keyword.get(options, :dimensions, 64)
      normalization = Keyword.get(options, :normalization, :none)

      # Get the appropriate encoder implementation based on input type
      encoder_impl = get_encoder_implementation(symbolic_input)

      # Apply the encoder to get the raw vector
      {raw_vector, metadata} = encoder_impl.encode(symbolic_input, dimensions)

      # Apply normalization if specified
      normalized_vector = apply_normalization(raw_vector, normalization)

      # Calculate confidence based on encoding characteristics
      confidence = calculate_confidence(symbolic_input, raw_vector, metadata)

      # Return the neural representation and confidence
      {%{vector: normalized_vector, metadata: metadata}, confidence}
    end

    @doc """
    Returns the appropriate encoder implementation based on the input type.
    """
    @spec get_encoder_implementation(symbolic_input()) :: module()
    defp get_encoder_implementation(input) when is_map(input) do
      # For map inputs (like knowledge graphs, fact sets)
      __MODULE__.MapEncoder
    end

    defp get_encoder_implementation(input) when is_list(input) do
      # For list inputs (like rule sets, sequences)
      __MODULE__.ListEncoder
    end

    defp get_encoder_implementation(input) when is_tuple(input) do
      # For tuple inputs (like logical expressions)
      __MODULE__.TupleEncoder
    end

    defp get_encoder_implementation(input) when is_atom(input) or is_binary(input) do
      # For atomic symbols or strings
      __MODULE__.AtomicEncoder
    end

    @doc """
    Applies the specified normalization to the vector.
    """
    @spec apply_normalization(list(float()), atom()) :: list(float())
    defp apply_normalization(vector, :none), do: vector

    defp apply_normalization(vector, :l2) do
      # L2 normalization (unit vector)
      magnitude = :math.sqrt(Enum.sum(Enum.map(vector, fn x -> x * x end)))
      Enum.map(vector, fn x -> x / magnitude end)
    end

    defp apply_normalization(vector, :l1) do
      # L1 normalization
      sum = Enum.sum(Enum.map(vector, &abs/1))
      Enum.map(vector, fn x -> x / sum end)
    end

    defp apply_normalization(vector, :minmax) do
      # Min-max normalization
      min_val = Enum.min(vector)
      max_val = Enum.max(vector)
      range = max_val - min_val

      if range == 0 do
        List.duplicate(0.5, length(vector))
      else
        Enum.map(vector, fn x -> (x - min_val) / range end)
      end
    end

    @doc """
    Calculates the confidence of the encoding based on input characteristics.
    """
    @spec calculate_confidence(symbolic_input(), list(float()), map()) :: float()
    defp calculate_confidence(input, vector, metadata) do
      # Placeholder for confidence calculation
      # In a real implementation, this would consider factors like:
      # - Complexity of input structure
      # - Known limitations of encoder for certain patterns
      # - Historical performance on similar inputs
      # - Coverage of the input's semantic space
      0.95
    end
  end

  defmodule Decoder do
    @moduledoc """
    Decoder module for converting neural representations to symbolic representations.
    """

    @type neural_input :: %{
            vector: list(float()),
            metadata: map()
          }
    @type symbolic_output :: map() | list() | tuple() | atom() | String.t()
    @type decoder_options :: [
            model: atom(),
            output_type: :map | :list | :tuple | :atom | :string,
            threshold: float()
          ]

    @doc """
    Decodes a neural representation into a symbolic representation.

    Options:
    - model: The decoding model to use (:transformer, :classification, :rule_extraction, etc.)
    - output_type: The type of symbolic output to generate
    - threshold: Confidence threshold for including elements in the output
    """
    @spec decode(neural_input(), decoder_options()) :: {symbolic_output(), float()}
    def decode(neural_input, options \\ []) do
      model = Keyword.get(options, :model, :default)
      output_type = Keyword.get(options, :output_type, :map)
      threshold = Keyword.get(options, :threshold, 0.5)

      # Get the appropriate decoder implementation based on desired output type
      decoder_impl = get_decoder_implementation(output_type)

      # Apply the decoder to get the symbolic representation
      {raw_symbolic, metadata} = decoder_impl.decode(neural_input, threshold)

      # Calculate confidence based on decoding characteristics
      confidence = calculate_confidence(neural_input, raw_symbolic, metadata)

      # Return the symbolic representation and confidence
      {raw_symbolic, confidence}
    end

    @doc """
    Returns the appropriate decoder implementation based on the desired output type.
    """
    @spec get_decoder_implementation(atom()) :: module()
    defp get_decoder_implementation(:map) do
      # For map outputs (like knowledge graphs, fact sets)
      __MODULE__.MapDecoder
    end

    defp get_decoder_implementation(:list) do
      # For list outputs (like rule sets, sequences)
      __MODULE__.ListDecoder
    end

    defp get_decoder_implementation(:tuple) do
      # For tuple outputs (like logical expressions)
      __MODULE__.TupleDecoder
    end

    defp get_decoder_implementation(type) when type in [:atom, :string] do
      # For atomic symbols or strings
      __MODULE__.AtomicDecoder
    end

    @doc """
    Calculates the confidence of the decoding based on vector characteristics.
    """
    @spec calculate_confidence(neural_input(), symbolic_output(), map()) :: float()
    defp calculate_confidence(input, symbolic, metadata) do
      # Placeholder for confidence calculation
      # In a real implementation, this would consider factors like:
      # - Vector entropy
      # - Distance to known reference points
      # - Ambiguity in the mapping
      # - Historical performance on similar vectors
      0.85
    end
  end

  defmodule Metrics do
    @moduledoc """
    Metrics module for evaluating translation quality and confidence.
    """

    @doc """
    Measures translation fidelity by performing a roundtrip translation and
    comparing the original with the result.

    Returns a score between 0.0 and 1.0 representing fidelity.
    """
    @spec measure_roundtrip_fidelity(
            any(),
            Encoder.encoder_options(),
            Decoder.decoder_options()
          ) :: float()
    def measure_roundtrip_fidelity(original, encoder_options \\ [], decoder_options \\ []) do
      # Perform the roundtrip translation
      {neural, encoder_confidence} = Encoder.encode(original, encoder_options)
      {roundtrip, decoder_confidence} = Decoder.decode(neural, decoder_options)

      # Calculate semantic similarity between original and roundtrip
      similarity = semantic_similarity(original, roundtrip)

      # Combine similarity with confidence scores
      combined_score = similarity * encoder_confidence * decoder_confidence

      # Return the fidelity score
      combined_score
    end

    @doc """
    Estimates the semantic similarity between two symbolic representations.
    """
    @spec semantic_similarity(any(), any()) :: float()
    defp semantic_similarity(a, b) when a == b, do: 1.0

    defp semantic_similarity(a, b) do
      # Placeholder for semantic similarity calculation
      # This would be implemented based on the specific representation types
      # and domain-specific similarity metrics
      0.8
    end

    @doc """
    Calculates the confidence interval for a translation.
    """
    @spec confidence_interval(float(), pos_integer()) :: {float(), float()}
    def confidence_interval(confidence, sample_size \\ 1) do
      # Calculate 95% confidence interval using normal approximation
      # In a real implementation, this would be more sophisticated
      std_error = :math.sqrt((confidence * (1 - confidence)) / sample_size)
      margin = 1.96 * std_error

      {max(0.0, confidence - margin), min(1.0, confidence + margin)}
    end
  end

  @doc """
  Converts a symbolic representation to a neural representation with confidence.
  """
  @spec symbolic_to_neural(any(), keyword()) :: {map(), float()}
  def symbolic_to_neural(symbolic, options \\ []) do
    Encoder.encode(symbolic, options)
  end

  @doc """
  Converts a neural representation to a symbolic representation with confidence.
  """
  @spec neural_to_symbolic(map(), keyword()) :: {any(), float()}
  def neural_to_symbolic(neural, options \\ []) do
    Decoder.decode(neural, options)
  end

  @doc """
  Performs progressive refinement of a translation by applying feedback.
  """
  @spec refine_translation(any(), map(), keyword()) :: {any(), float()}
  def refine_translation(original, feedback, options \\ []) do
    iterations = Keyword.get(options, :iterations, 3)
    learning_rate = Keyword.get(options, :learning_rate, 0.1)

    # Start with initial translation
    {neural, _} = symbolic_to_neural(original)
    {symbolic, confidence} = neural_to_symbolic(neural)

    # Apply iterative refinement based on feedback
    refine_recursive(original, symbolic, neural, feedback, confidence, iterations, learning_rate)
  end

  @doc """
  Recursive helper for refine_translation that applies iterative refinement.
  """
  @spec refine_recursive(any(), any(), map(), map(), float(), non_neg_integer(), float()) ::
          {any(), float()}
  defp refine_recursive(_, symbolic, _, _, confidence, 0, _), do: {symbolic, confidence}

  defp refine_recursive(original, symbolic, neural, feedback, confidence, iterations, learning_rate) do
    # Adjust neural representation based on feedback
    adjusted_neural = apply_feedback(neural, feedback, learning_rate)

    # Generate new symbolic representation
    {new_symbolic, new_confidence} = neural_to_symbolic(adjusted_neural)

    # If confidence improved, continue with refinement
    if new_confidence > confidence do
      refine_recursive(
        original,
        new_symbolic,
        adjusted_neural,
        feedback,
        new_confidence,
        iterations - 1,
        learning_rate
      )
    else
      # Return the best result so far
      {symbolic, confidence}
    end
  end

  @doc """
  Applies feedback to adjust a neural representation.
  """
  @spec apply_feedback(map(), map(), float()) :: map()
  defp apply_feedback(neural, feedback, learning_rate) do
    # Placeholder for feedback application
    # This would adjust the neural representation based on the feedback
    # using the learning rate to control magnitude of changes
    neural
  end

  @doc """
  Evaluates the overall quality of the translation framework on a test dataset.
  """
  @spec evaluate_framework(list({any(), any()})) :: map()
  def evaluate_framework(test_pairs) do
    results =
      Enum.map(test_pairs, fn {symbolic, expected_neural} ->
        # Forward translation
        {actual_neural, forward_confidence} = symbolic_to_neural(symbolic)
        forward_error = vector_distance(actual_neural.vector, expected_neural.vector)

        # Backward translation
        {reconstructed, backward_confidence} = neural_to_symbolic(expected_neural)
        backward_similarity = Metrics.semantic_similarity(symbolic, reconstructed)

        # Roundtrip
        roundtrip_fidelity = Metrics.measure_roundtrip_fidelity(symbolic)

        %{
          forward_error: forward_error,
          forward_confidence: forward_confidence,
          backward_similarity: backward_similarity,
          backward_confidence: backward_confidence,
          roundtrip_fidelity: roundtrip_fidelity
        }
      end)

    # Calculate aggregate metrics
    %{
      mean_forward_error: mean(Enum.map(results, & &1.forward_error)),
      mean_forward_confidence: mean(Enum.map(results, & &1.forward_confidence)),
      mean_backward_similarity: mean(Enum.map(results, & &1.backward_similarity)),
      mean_backward_confidence: mean(Enum.map(results, & &1.backward_confidence)),
      mean_roundtrip_fidelity: mean(Enum.map(results, & &1.roundtrip_fidelity))
    }
  end

  @doc """
  Calculates the Euclidean distance between two vectors.
  """
  @spec vector_distance(list(number()), list(number())) :: float()
  defp vector_distance(v1, v2) do
    Enum.zip(v1, v2)
    |> Enum.map(fn {a, b} -> (a - b) * (a - b) end)
    |> Enum.sum()
    |> :math.sqrt()
  end

  @doc """
  Calculates the mean of a list of numbers.
  """
  @spec mean(list(number())) :: float()
  defp mean(numbers) do
    Enum.sum(numbers) / length(numbers)
  end
end