defmodule Dspy.ModelSaturationFramework do
  @moduledoc """
  Advanced model saturation testing framework that systematically explores
  model capabilities across multiple dimensions to find performance limits
  and capability boundaries.
  """

  defstruct [
    :capability_dimensions,
    :saturation_metrics,
    :exploration_strategy,
    :performance_boundaries,
    :capability_map,
    :saturation_history
  ]

  @type saturation_level :: :unsaturated | :approaching_saturation | :saturated | :oversaturated

  def new(opts \\ []) do
    %__MODULE__{
      capability_dimensions: initialize_capability_dimensions(opts),
      saturation_metrics: initialize_saturation_metrics(),
      exploration_strategy: Keyword.get(opts, :strategy, :adaptive_boundary_search),
      performance_boundaries: %{},
      capability_map: %{},
      saturation_history: []
    }
  end

  def evaluate_model_saturation(framework, model_module, evaluation_config \\ %{}) do
    _ = """
    Comprehensively evaluate model saturation across all capability dimensions
    """
    
    # 1. Multi-dimensional capability assessment
    {capability_assessments, updated_framework} = 
      assess_all_capability_dimensions(framework, model_module, evaluation_config)
    
    # 2. Boundary exploration
    {boundary_results, framework_with_boundaries} =
      explore_performance_boundaries(updated_framework, model_module, capability_assessments)
    
    # 3. Saturation level determination
    saturation_analysis = determine_saturation_levels(framework_with_boundaries, boundary_results)
    
    # 4. Generate comprehensive saturation report
    saturation_report = generate_saturation_report(saturation_analysis, capability_assessments, boundary_results)
    
    final_framework = record_saturation_evaluation(framework_with_boundaries, saturation_report)
    
    {saturation_report, final_framework}
  end

  def track_saturation_over_time(framework, model_module, tracking_config \\ %{}) do
    _ = """
    Track how model saturation changes over extended evaluation periods
    """
    
    duration = Map.get(tracking_config, :duration_minutes, 60)
    _sampling_interval = Map.get(tracking_config, :sampling_interval_seconds, 300) # 5 minutes
    
    tracking_results = %{
      start_time: DateTime.utc_now(),
      duration: duration,
      sampling_points: [],
      saturation_trajectory: [],
      capability_evolution: %{}
    }
    
    run_saturation_tracking_loop(framework, model_module, tracking_results, tracking_config)
  end

  def identify_capability_gaps(framework, target_capabilities \\ nil) do
    _ = """
    Identify gaps in model capabilities and suggest targeted evaluations
    """
    
    target_caps = target_capabilities || get_comprehensive_capability_set()
    
    assessed_capabilities = Map.keys(framework.capability_map)
    missing_capabilities = target_caps -- assessed_capabilities
    
    underperforming_capabilities = 
      framework.capability_map
      |> Enum.filter(fn {_cap, results} -> 
           results.saturation_level in [:unsaturated, :approaching_saturation] and
           results.max_performance < 0.7
         end)
      |> Enum.map(fn {cap, _} -> cap end)
    
    %{
      missing_capabilities: missing_capabilities,
      underperforming_capabilities: underperforming_capabilities,
      suggested_evaluations: generate_targeted_evaluations(missing_capabilities, underperforming_capabilities),
      coverage_score: length(assessed_capabilities) / length(target_caps)
    }
  end

  def adaptive_difficulty_progression(framework, model_module, capability) do
    _ = """
    Implement adaptive difficulty progression to find exact saturation points
    """
    
    # Start with current boundary knowledge
    current_boundary = Map.get(framework.performance_boundaries, capability, %{min: 0.0, max: 1.0})
    
    # Binary search approach to find saturation point
    saturation_point = binary_search_saturation_point(
      model_module, 
      capability, 
      current_boundary.min, 
      current_boundary.max,
      precision: 0.05
    )
    
    # Validate saturation point with multiple samples
    validation_results = validate_saturation_point(model_module, capability, saturation_point)
    
    %{
      capability: capability,
      saturation_difficulty: saturation_point,
      validation_confidence: validation_results.confidence,
      performance_at_saturation: validation_results.performance,
      samples_used: validation_results.sample_count
    }
  end

  def generate_saturation_interventions(framework) do
    _ = """
    Generate interventions to push model beyond current saturation points
    """
    
    interventions = []
    
    # For each capability dimension
    for {capability, results} <- framework.capability_map do
      case results.saturation_level do
        :saturated ->
          _interventions = interventions ++ generate_breakthrough_interventions(capability, results)
        
        :approaching_saturation ->
          _interventions = interventions ++ generate_optimization_interventions(capability, results)
        
        :unsaturated ->
          _interventions = interventions ++ generate_exploration_interventions(capability, results)
        
        _ -> interventions
      end
    end
    
    # Sort by potential impact
    Enum.sort_by(interventions, &(&1.potential_impact), :desc)
  end

  defp initialize_capability_dimensions(_opts) do
    %{
      # Cognitive capabilities
      reasoning: %{
        sub_dimensions: [:logical, :causal, :analogical, :counterfactual, :moral],
        measurement_scales: [:accuracy, :consistency, :depth, :speed],
        saturation_indicators: [:performance_plateau, :error_pattern_stability, :confidence_convergence]
      },
      
      memory: %{
        sub_dimensions: [:working_memory, :episodic, :semantic, :procedural],
        measurement_scales: [:capacity, :retention, :retrieval_speed, :accuracy],
        saturation_indicators: [:capacity_limits, :interference_patterns, :forgetting_curves]
      },
      
      learning: %{
        sub_dimensions: [:few_shot, :in_context, :transfer, :meta_learning],
        measurement_scales: [:adaptation_speed, :generalization, :retention, :efficiency],
        saturation_indicators: [:learning_curve_plateaus, :transfer_limits, :adaptation_failures]
      },
      
      creativity: %{
        sub_dimensions: [:novelty, :usefulness, :surprise, :aesthetic],
        measurement_scales: [:originality_scores, :coherence, :diversity, :appropriateness],
        saturation_indicators: [:repetition_patterns, :novelty_decline, :creative_exhaustion]
      },
      
      language: %{
        sub_dimensions: [:comprehension, :generation, :translation, :style_adaptation],
        measurement_scales: [:fluency, :accuracy, :coherence, :style_consistency],
        saturation_indicators: [:grammar_plateaus, :semantic_limits, :pragmatic_boundaries]
      },
      
      problem_solving: %{
        sub_dimensions: [:decomposition, :planning, :optimization, :constraint_satisfaction],
        measurement_scales: [:solution_quality, :efficiency, :robustness, :scalability],
        saturation_indicators: [:complexity_limits, :solution_repetition, :planning_failures]
      },
      
      metacognition: %{
        sub_dimensions: [:self_awareness, :uncertainty_estimation, :strategy_selection, :performance_monitoring],
        measurement_scales: [:calibration, :introspection_accuracy, :strategy_effectiveness, :monitoring_precision],
        saturation_indicators: [:calibration_plateaus, :overconfidence_patterns, :strategy_rigidity]
      }
    }
  end

  defp initialize_saturation_metrics do
    %{
      # Performance-based metrics
      accuracy_plateau_threshold: 0.02,
      performance_variance_threshold: 0.05,
      improvement_rate_threshold: 0.001,
      
      # Behavioral metrics  
      response_pattern_stability: 0.8,
      error_type_consistency: 0.7,
      confidence_calibration_threshold: 0.9,
      
      # Temporal metrics
      plateau_duration_threshold: 10, # Number of evaluations
      saturation_confirmation_samples: 5,
      boundary_validation_samples: 3
    }
  end

  defp assess_all_capability_dimensions(framework, model_module, evaluation_config) do
    capability_assessments = %{}
    updated_framework = framework
    
    for {dimension, dimension_config} <- framework.capability_dimensions, reduce: {capability_assessments, updated_framework} do
      {assessments, current_framework} ->
        
        # Assess each sub-dimension
        sub_assessments = %{}
        
        for sub_dim <- dimension_config.sub_dimensions, reduce: sub_assessments do
          sub_acc ->
            assessment = assess_capability_dimension(
              model_module, 
              {dimension, sub_dim}, 
              dimension_config,
              evaluation_config
            )
            Map.put(sub_acc, sub_dim, assessment)
        end
        
        # Aggregate dimension assessment
        dimension_assessment = aggregate_dimension_assessment(sub_assessments, dimension_config)
        
        updated_assessments = Map.put(assessments, dimension, dimension_assessment)
        updated_capability_map = Map.put(current_framework.capability_map, dimension, dimension_assessment)
        updated_framework_state = %{current_framework | capability_map: updated_capability_map}
        
        {updated_assessments, updated_framework_state}
    end
  end

  defp assess_capability_dimension(model_module, {dimension, sub_dimension}, dimension_config, evaluation_config) do
    # Generate progressive difficulty challenges for this specific capability
    challenges = generate_capability_challenges(dimension, sub_dimension, evaluation_config)
    
    results = %{
      performance_curve: [],
      saturation_point: nil,
      max_performance: 0.0,
      error_patterns: [],
      confidence_patterns: [],
      saturation_level: :unsaturated
    }
    
    # Evaluate across difficulty spectrum
    for {difficulty, challenge_set} <- challenges, reduce: results do
      acc_results ->
        challenge_results = evaluate_challenge_set(model_module, challenge_set)
        
        performance_point = %{
          difficulty: difficulty,
          accuracy: challenge_results.accuracy,
          confidence: challenge_results.confidence,
          response_time: challenge_results.response_time,
          error_types: challenge_results.error_types
        }
        
        updated_curve = acc_results.performance_curve ++ [performance_point]
        updated_max = max(acc_results.max_performance, challenge_results.accuracy)
        
        # Check for saturation indicators
        saturation_level = detect_saturation_level(updated_curve, dimension_config)
        
        %{acc_results | 
          performance_curve: updated_curve,
          max_performance: updated_max,
          saturation_level: saturation_level,
          error_patterns: acc_results.error_patterns ++ challenge_results.error_types,
          confidence_patterns: acc_results.confidence_patterns ++ [challenge_results.confidence]
        }
    end
  end

  defp generate_capability_challenges(dimension, sub_dimension, evaluation_config) do
    base_challenges = get_base_challenges_for_capability(dimension, sub_dimension)
    difficulty_levels = Map.get(evaluation_config, :difficulty_levels, 10)
    
    # Generate challenges across difficulty spectrum
    for difficulty <- 1..difficulty_levels, into: %{} do
      challenge_set = adapt_challenges_for_difficulty(base_challenges, difficulty / difficulty_levels)
      {difficulty / difficulty_levels, challenge_set}
    end
  end

  defp detect_saturation_level(performance_curve, _dimension_config) do
    if length(performance_curve) < 3 do
      :unsaturated
    else
      recent_performances = performance_curve |> Enum.take(-3) |> Enum.map(&(&1.accuracy))
      performance_variance = calculate_variance(recent_performances)
      
      latest_performance = List.last(performance_curve).accuracy
      
      cond do
        performance_variance < 0.01 and latest_performance > 0.95 -> :oversaturated
        performance_variance < 0.02 and latest_performance > 0.85 -> :saturated
        performance_variance < 0.05 and latest_performance > 0.7 -> :approaching_saturation
        true -> :unsaturated
      end
    end
  end

  defp explore_performance_boundaries(framework, model_module, capability_assessments) do
    boundary_results = %{}
    
    for {capability, assessment} <- capability_assessments, reduce: {boundary_results, framework} do
      {boundaries, current_framework} ->
        
        # Find precise performance boundary for this capability
        boundary = find_performance_boundary(model_module, capability, assessment)
        
        updated_boundaries = Map.put(boundaries, capability, boundary)
        updated_framework_boundaries = Map.put(current_framework.performance_boundaries, capability, boundary)
        updated_framework = %{current_framework | performance_boundaries: updated_framework_boundaries}
        
        {updated_boundaries, updated_framework}
    end
  end

  defp find_performance_boundary(_model_module, _capability, assessment) do
    # Find the difficulty level where performance drops below threshold
    performance_curve = assessment.performance_curve
    
    boundary_point = 
      Enum.find(performance_curve, fn point -> 
        point.accuracy < 0.5 
      end)
    
    case boundary_point do
      nil -> 
        # No boundary found within tested range
        %{
          type: :no_boundary_found,
          max_tested_difficulty: performance_curve |> List.last() |> Map.get(:difficulty, 1.0),
          performance_at_max: performance_curve |> List.last() |> Map.get(:accuracy, 0.0)
        }
      
      boundary ->
        %{
          type: :boundary_found,
          boundary_difficulty: boundary.difficulty,
          performance_at_boundary: boundary.accuracy,
          boundary_confidence: boundary.confidence
        }
    end
  end

  defp determine_saturation_levels(framework, _boundary_results) do
    overall_saturation = %{
      global_saturation_level: :unsaturated,
      saturated_capabilities: [],
      approaching_saturation_capabilities: [],
      unsaturated_capabilities: [],
      saturation_distribution: %{}
    }
    
    capability_saturation_levels = 
      for {capability, assessment} <- framework.capability_map, into: %{} do
        {capability, assessment.saturation_level}
      end
    
    # Calculate distribution
    distribution = 
      capability_saturation_levels
      |> Map.values()
      |> Enum.frequencies()
    
    # Determine global saturation level
    saturated_count = Map.get(distribution, :saturated, 0)
    approaching_count = Map.get(distribution, :approaching_saturation, 0)
    total_capabilities = map_size(capability_saturation_levels)
    
    global_level = 
      cond do
        saturated_count / total_capabilities > 0.8 -> :saturated
        (saturated_count + approaching_count) / total_capabilities > 0.6 -> :approaching_saturation
        true -> :unsaturated
      end
    
    %{overall_saturation | 
      global_saturation_level: global_level,
      saturated_capabilities: get_capabilities_by_level(capability_saturation_levels, :saturated),
      approaching_saturation_capabilities: get_capabilities_by_level(capability_saturation_levels, :approaching_saturation),
      unsaturated_capabilities: get_capabilities_by_level(capability_saturation_levels, :unsaturated),
      saturation_distribution: distribution
    }
  end

  defp generate_saturation_report(saturation_analysis, capability_assessments, boundary_results) do
    %{
      timestamp: DateTime.utc_now(),
      global_saturation: saturation_analysis,
      capability_details: capability_assessments,
      performance_boundaries: boundary_results,
      recommendations: generate_recommendations(saturation_analysis, capability_assessments),
      next_evaluation_suggestions: suggest_next_evaluations(saturation_analysis, boundary_results),
      saturation_score: calculate_overall_saturation_score(saturation_analysis, capability_assessments)
    }
  end

  # Utility functions
  defp calculate_variance(values) do
    if length(values) <= 1 do
      0.0
    else
      mean = Enum.sum(values) / length(values)
      squared_diffs = Enum.map(values, fn x -> (x - mean) * (x - mean) end)
      Enum.sum(squared_diffs) / (length(values) - 1)
    end
  end

  defp get_capabilities_by_level(capability_levels, target_level) do
    capability_levels
    |> Enum.filter(fn {_cap, level} -> level == target_level end)
    |> Enum.map(fn {cap, _level} -> cap end)
  end

  defp calculate_overall_saturation_score(_saturation_analysis, capability_assessments) do
    # Weighted score based on capability importance and saturation levels
    capability_weights = %{
      reasoning: 0.25,
      problem_solving: 0.20,
      learning: 0.15,
      language: 0.15,
      memory: 0.10,
      creativity: 0.10,
      metacognition: 0.05
    }
    
    weighted_scores = 
      for {capability, assessment} <- capability_assessments do
        weight = Map.get(capability_weights, capability, 0.1)
        saturation_score = saturation_level_to_score(assessment.saturation_level)
        performance_score = assessment.max_performance
        
        weight * (0.6 * saturation_score + 0.4 * performance_score)
      end
    
    Enum.sum(weighted_scores)
  end

  defp saturation_level_to_score(:oversaturated), do: 1.0
  defp saturation_level_to_score(:saturated), do: 0.8
  defp saturation_level_to_score(:approaching_saturation), do: 0.6
  defp saturation_level_to_score(:unsaturated), do: 0.3

  # Placeholder implementations for complex functions
  defp evaluate_challenge_set(_model_module, _challenge_set) do
    %{
      accuracy: :rand.uniform(),
      confidence: :rand.uniform(),
      response_time: :rand.uniform() * 1000,
      error_types: [:logical_error, :factual_error] |> Enum.take_random(1)
    }
  end

  defp get_base_challenges_for_capability(_dimension, _sub_dimension) do
    # Return base challenge templates
    []
  end

  defp adapt_challenges_for_difficulty(base_challenges, _difficulty) do
    base_challenges
  end

  defp aggregate_dimension_assessment(sub_assessments, _dimension_config) do
    # Aggregate sub-dimension assessments into overall dimension assessment
    max_performance = 
      sub_assessments
      |> Map.values()
      |> Enum.map(&(&1.max_performance))
      |> Enum.max(fn -> 0.0 end)
    
    saturation_levels = 
      sub_assessments
      |> Map.values()
      |> Enum.map(&(&1.saturation_level))
    
    overall_saturation = determine_overall_saturation_level(saturation_levels)
    
    %{
      max_performance: max_performance,
      saturation_level: overall_saturation,
      sub_dimension_results: sub_assessments,
      performance_curve: [],
      error_patterns: [],
      confidence_patterns: []
    }
  end

  defp determine_overall_saturation_level(levels) do
    level_counts = Enum.frequencies(levels)
    
    cond do
      Map.get(level_counts, :saturated, 0) >= length(levels) / 2 -> :saturated
      Map.get(level_counts, :approaching_saturation, 0) >= length(levels) / 2 -> :approaching_saturation
      true -> :unsaturated
    end
  end

  defp record_saturation_evaluation(framework, report) do
    updated_history = [report | framework.saturation_history]
    %{framework | saturation_history: updated_history}
  end

  defp generate_recommendations(_saturation_analysis, _capability_assessments) do
    [
      "Continue evaluation of unsaturated capabilities",
      "Implement interventions for saturated capabilities",
      "Monitor capability evolution over time"
    ]
  end

  defp suggest_next_evaluations(_saturation_analysis, _boundary_results) do
    [
      "Extended temporal tracking",
      "Cross-capability interference testing",
      "Intervention effectiveness evaluation"
    ]
  end

  # Placeholder functions for advanced features
  defp run_saturation_tracking_loop(_framework, _model_module, _tracking_results, _config), do: %{}
  defp get_comprehensive_capability_set, do: [:reasoning, :memory, :learning, :creativity, :language, :problem_solving, :metacognition]
  defp generate_targeted_evaluations(_missing, _underperforming), do: []
  defp binary_search_saturation_point(_model, _capability, _min, _max, _opts), do: 0.5
  defp validate_saturation_point(_model, _capability, _point), do: %{confidence: 0.8, performance: 0.7, sample_count: 5}
  defp generate_breakthrough_interventions(_capability, _results), do: []
  defp generate_optimization_interventions(_capability, _results), do: []
  defp generate_exploration_interventions(_capability, _results), do: []
end