defmodule Automata.Reasoning.Cognitive.NeuralSymbolic.VerificationSuite do
  @moduledoc """
  Integration Verification Suite for Neural-Symbolic Integration

  This module provides verification capabilities for neural-symbolic integration:
  - Symbolic verification of neural outputs with consistency checks
  - Neural verification of symbolic reasoning with anomaly detection
  - Hybrid reasoning correctness metrics
  """

  alias Automata.Reasoning.Cognitive.NeuralSymbolic.TranslationFramework
  alias Automata.Reasoning.Cognitive.ContextualReasoning.InferenceEngine.ContextualInference

  defmodule SymbolicVerification do
    @moduledoc """
    Provides methods for verifying neural outputs using symbolic reasoning.
    """

    @type verification_result :: %{
            valid: boolean(),
            consistency_score: float(),
            violations: list(map()),
            confidence: float()
          }

    @doc """
    Verifies a neural output by translating it to symbolic form and
    checking for logical consistency using symbolic reasoning.
    """
    @spec verify_neural_output(map(), keyword()) :: verification_result
    def verify_neural_output(neural_output, options \\ []) do
      # Convert neural representation to symbolic
      {symbolic, confidence} = TranslationFramework.neural_to_symbolic(neural_output, options)

      # Apply symbolic consistency checks
      consistency_result = check_symbolic_consistency(symbolic, options)

      # Return verification result
      %{
        valid: consistency_result.consistent,
        consistency_score: consistency_result.score,
        violations: consistency_result.violations,
        confidence: confidence * consistency_result.confidence
      }
    end

    @doc """
    Checks a symbolic representation for logical consistency.
    """
    @spec check_symbolic_consistency(any(), keyword()) :: %{
            consistent: boolean(),
            score: float(),
            violations: list(map()),
            confidence: float()
          }
    defp check_symbolic_consistency(symbolic, options) do
      # Apply various consistency checks based on representation type
      checks =
        [
          check_internal_consistency(symbolic),
          check_domain_constraints(symbolic, options[:domain_constraints]),
          check_formal_properties(symbolic, options[:formal_properties])
        ]

      # Aggregate results from all checks
      violations = Enum.flat_map(checks, & &1.violations)
      consistent = Enum.all?(checks, & &1.consistent)
      
      # Calculate overall consistency score as weighted average
      weights = [0.5, 0.3, 0.2]  # Weights for different check types
      scores = Enum.map(checks, & &1.score)
      overall_score = weighted_average(scores, weights)
      
      # Calculate confidence based on check coverage and quality
      confidence = calculate_verification_confidence(symbolic, checks)

      %{
        consistent: consistent,
        score: overall_score,
        violations: violations,
        confidence: confidence
      }
    end

    @doc """
    Checks internal logical consistency of symbolic representation.
    """
    @spec check_internal_consistency(any()) :: %{
            consistent: boolean(),
            score: float(),
            violations: list(map())
          }
    defp check_internal_consistency(symbolic) do
      # This would implement logic to detect contradictions
      # and ensure logical consistency within the symbolic representation
      
      # For now, return a placeholder result
      %{
        consistent: true,
        score: 0.95,
        violations: []
      }
    end

    @doc """
    Checks conformance to domain-specific constraints.
    """
    @spec check_domain_constraints(any(), list() | nil) :: %{
            consistent: boolean(),
            score: float(),
            violations: list(map())
          }
    defp check_domain_constraints(_symbolic, nil) do
      # If no domain constraints provided, consider check passed
      %{consistent: true, score: 1.0, violations: []}
    end

    defp check_domain_constraints(symbolic, constraints) do
      # This would validate the symbolic representation against
      # domain-specific constraints (e.g., physical laws, business rules)
      
      # For now, return a placeholder result
      %{
        consistent: true,
        score: 0.9,
        violations: []
      }
    end

    @doc """
    Checks formal properties of the symbolic representation.
    """
    @spec check_formal_properties(any(), list() | nil) :: %{
            consistent: boolean(),
            score: float(),
            violations: list(map())
          }
    defp check_formal_properties(_symbolic, nil) do
      # If no formal properties provided, consider check passed
      %{consistent: true, score: 1.0, violations: []}
    end

    defp check_formal_properties(symbolic, properties) do
      # This would verify formal properties like:
      # - Termination guarantees for procedures
      # - Safety properties
      # - Liveness properties
      
      # For now, return a placeholder result
      %{
        consistent: true,
        score: 0.88,
        violations: []
      }
    end

    @doc """
    Calculates verification confidence based on check coverage and quality.
    """
    @spec calculate_verification_confidence(any(), list(map())) :: float()
    defp calculate_verification_confidence(symbolic, checks) do
      # In a real implementation, this would consider:
      # - Comprehensiveness of checks for this type of symbolic representation
      # - Coverage of all aspects of the representation
      # - Quality of the checks performed
      0.9
    end
  end

  defmodule NeuralVerification do
    @moduledoc """
    Provides methods for verifying symbolic reasoning using neural approaches.
    """

    @type verification_result :: %{
            valid: boolean(),
            anomaly_score: float(),
            anomalies: list(map()),
            confidence: float()
          }

    @doc """
    Verifies symbolic reasoning by translating to neural form and
    applying anomaly detection techniques.
    """
    @spec verify_symbolic_reasoning(any(), list(tuple()), keyword()) :: verification_result
    def verify_symbolic_reasoning(symbolic_result, reasoning_trace, options \\ []) do
      # Convert symbolic result to neural
      {neural, confidence} = TranslationFramework.symbolic_to_neural(symbolic_result, options)

      # Apply neural anomaly detection
      anomaly_result = detect_reasoning_anomalies(neural, reasoning_trace, options)

      # Return verification result
      %{
        valid: anomaly_result.valid,
        anomaly_score: anomaly_result.score,
        anomalies: anomaly_result.anomalies,
        confidence: confidence * anomaly_result.confidence
      }
    end

    @doc """
    Detects anomalies in reasoning using neural techniques.
    """
    @spec detect_reasoning_anomalies(map(), list(tuple()), keyword()) :: %{
            valid: boolean(),
            score: float(),
            anomalies: list(map()),
            confidence: float()
          }
    defp detect_reasoning_anomalies(neural_result, reasoning_trace, options) do
      # Apply various anomaly detection techniques
      detections =
        [
          detect_statistical_anomalies(neural_result, reasoning_trace),
          detect_pattern_anomalies(neural_result, reasoning_trace),
          detect_consistency_anomalies(neural_result, reasoning_trace)
        ]

      # Aggregate results from all detection methods
      anomalies = Enum.flat_map(detections, & &1.anomalies)
      valid = Enum.all?(detections, & &1.valid)
      
      # Calculate overall anomaly score
      weights = [0.4, 0.4, 0.2]  # Weights for different detection types
      scores = Enum.map(detections, & &1.score)
      overall_score = weighted_average(scores, weights)
      
      # Calculate confidence in anomaly detection
      confidence = calculate_detection_confidence(neural_result, reasoning_trace, detections)

      %{
        valid: valid,
        score: overall_score,
        anomalies: anomalies,
        confidence: confidence
      }
    end

    @doc """
    Detects statistical anomalies in the neural result.
    """
    @spec detect_statistical_anomalies(map(), list(tuple())) :: %{
            valid: boolean(),
            score: float(),
            anomalies: list(map())
          }
    defp detect_statistical_anomalies(neural_result, reasoning_trace) do
      # This would implement statistical analysis to detect outliers
      # and unusual patterns in the neural representation
      
      # For now, return a placeholder result
      %{
        valid: true,
        score: 0.02,  # Lower score is better for anomalies
        anomalies: []
      }
    end

    @doc """
    Detects pattern anomalies in the neural result.
    """
    @spec detect_pattern_anomalies(map(), list(tuple())) :: %{
            valid: boolean(),
            score: float(),
            anomalies: list(map())
          }
    defp detect_pattern_anomalies(neural_result, reasoning_trace) do
      # This would use pattern recognition to identify deviations
      # from expected reasoning patterns
      
      # For now, return a placeholder result
      %{
        valid: true,
        score: 0.05,  # Lower score is better for anomalies
        anomalies: []
      }
    end

    @doc """
    Detects consistency anomalies across the reasoning trace.
    """
    @spec detect_consistency_anomalies(map(), list(tuple())) :: %{
            valid: boolean(),
            score: float(),
            anomalies: list(map())
          }
    defp detect_consistency_anomalies(neural_result, reasoning_trace) do
      # This would verify consistency across reasoning steps
      # looking for jumps or discontinuities
      
      # For now, return a placeholder result
      %{
        valid: true,
        score: 0.03,  # Lower score is better for anomalies
        anomalies: []
      }
    end

    @doc """
    Calculates confidence in anomaly detection results.
    """
    @spec calculate_detection_confidence(map(), list(tuple()), list(map())) :: float()
    defp calculate_detection_confidence(neural_result, reasoning_trace, detections) do
      # In a real implementation, this would consider:
      # - Robustness of detection methods for this type of reasoning
      # - Coverage of different anomaly types
      # - Quality of the detection performed
      0.85
    end
  end

  defmodule HybridVerification do
    @moduledoc """
    Combines symbolic and neural verification approaches for comprehensive assessment.
    """

    @type verification_result :: %{
            valid: boolean(),
            score: float(),
            issues: list(map()),
            confidence: float()
          }

    @doc """
    Verifies a reasoning process using both symbolic and neural techniques.
    """
    @spec verify_hybrid_reasoning(any(), map(), list(tuple()), keyword()) :: verification_result
    def verify_hybrid_reasoning(symbolic_result, neural_result, reasoning_trace, options \\ []) do
      # Apply symbolic verification
      symbolic_verification = 
        SymbolicVerification.verify_neural_output(neural_result, options)

      # Apply neural verification
      neural_verification = 
        NeuralVerification.verify_symbolic_reasoning(symbolic_result, reasoning_trace, options)

      # Combine results with cross-verification
      combined_issues = 
        symbolic_verification.violations ++ neural_verification.anomalies

      # Calculate combined validity and confidence
      valid = symbolic_verification.valid and neural_verification.valid
      
      # Use symbolic consistency and inverse of anomaly score for overall score
      overall_score = (symbolic_verification.consistency_score + (1 - neural_verification.anomaly_score)) / 2
      
      # Combined confidence is geometric mean of individual confidences
      combined_confidence = 
        :math.sqrt(symbolic_verification.confidence * neural_verification.confidence)

      %{
        valid: valid,
        score: overall_score,
        issues: combined_issues,
        confidence: combined_confidence
      }
    end

    @doc """
    Verifies reasoning under adversarial conditions to test robustness.
    """
    @spec verify_under_adversarial_conditions(any(), list(tuple()), keyword()) :: verification_result
    def verify_under_adversarial_conditions(reasoning_result, reasoning_trace, options \\ []) do
      # Generate adversarial perturbations
      perturbations = generate_adversarial_perturbations(reasoning_result, options)
      
      # Verify each perturbed version
      results = Enum.map(perturbations, fn perturbed ->
        case perturbed.type do
          :symbolic ->
            # Verify perturbed symbolic result
            {neural, _} = TranslationFramework.symbolic_to_neural(perturbed.result)
            verify_hybrid_reasoning(perturbed.result, neural, reasoning_trace, options)
            
          :neural ->
            # Verify perturbed neural result
            {symbolic, _} = TranslationFramework.neural_to_symbolic(perturbed.result)
            verify_hybrid_reasoning(symbolic, perturbed.result, reasoning_trace, options)
        end
      end)
      
      # Calculate robustness score as percentage of valid results under perturbation
      valid_count = Enum.count(results, & &1.valid)
      robustness_score = valid_count / length(results)
      
      # Collect all issues from failed verifications
      all_issues = results
      |> Enum.filter(& not &1.valid)
      |> Enum.flat_map(& &1.issues)
      
      # Calculate overall confidence
      avg_confidence = mean(Enum.map(results, & &1.confidence))

      %{
        valid: robustness_score > options[:robustness_threshold] || 0.7,
        score: robustness_score,
        issues: all_issues,
        confidence: avg_confidence
      }
    end

    @doc """
    Generates adversarial perturbations to test reasoning robustness.
    """
    @spec generate_adversarial_perturbations(any(), keyword()) :: list(map())
    defp generate_adversarial_perturbations(reasoning_result, options) do
      # This would create variations of the reasoning result with small perturbations
      # to test the robustness of verification mechanisms
      
      # For now, return a placeholder result
      [
        %{type: :symbolic, result: reasoning_result, magnitude: 0.05},
        %{type: :symbolic, result: reasoning_result, magnitude: 0.1},
        %{type: :neural, result: %{vector: [0.1, 0.2], metadata: %{}}, magnitude: 0.05}
      ]
    end
  end

  @doc """
  Calculates the weighted average of a list of values.
  """
  @spec weighted_average(list(number()), list(number())) :: float()
  def weighted_average(values, weights) do
    Enum.zip(values, weights)
    |> Enum.map(fn {v, w} -> v * w end)
    |> Enum.sum()
  end

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

  @doc """
  Verifies a neural output using symbolic reasoning techniques.
  """
  @spec verify_neural_output(map(), keyword()) :: map()
  def verify_neural_output(neural_output, options \\ []) do
    SymbolicVerification.verify_neural_output(neural_output, options)
  end

  @doc """
  Verifies symbolic reasoning using neural anomaly detection.
  """
  @spec verify_symbolic_reasoning(any(), list(tuple()), keyword()) :: map()
  def verify_symbolic_reasoning(symbolic_result, reasoning_trace, options \\ []) do
    NeuralVerification.verify_symbolic_reasoning(symbolic_result, reasoning_trace, options)
  end

  @doc """
  Performs comprehensive verification using both symbolic and neural techniques.
  """
  @spec verify_hybrid_reasoning(any(), map(), list(tuple()), keyword()) :: map()
  def verify_hybrid_reasoning(symbolic_result, neural_result, reasoning_trace, options \\ []) do
    HybridVerification.verify_hybrid_reasoning(
      symbolic_result,
      neural_result,
      reasoning_trace,
      options
    )
  end

  @doc """
  Tests reasoning robustness under adversarial conditions.
  """
  @spec verify_under_adversarial_conditions(any(), list(tuple()), keyword()) :: map()
  def verify_under_adversarial_conditions(reasoning_result, reasoning_trace, options \\ []) do
    HybridVerification.verify_under_adversarial_conditions(
      reasoning_result,
      reasoning_trace,
      options
    )
  end
end