defmodule Dspy.NovelSystemGenerator do
  @moduledoc """
  Dynamic novel system generation module.

  This module dynamically generates novel reasoning systems by:
  - Combining existing reasoning patterns in new ways
  - Creating hybrid approaches from multiple techniques
  - Evolving reasoning strategies based on problem characteristics
  - Learning from successful and failed attempts
  """

  use Dspy.Module

  defstruct [
    :signature,
    :examples,
    :max_retries,
    :generation_strategies,
    :combination_patterns,
    :evolution_parameters,
    :training_data_store,
    :novelty_threshold,
    :success_criteria,
    :experiment_id
  ]

  @type generation_strategy ::
          :hybrid_combination
          | :pattern_evolution
          | :meta_reasoning
          | :constraint_driven
          | :goal_decomposition
          | :emergent_behavior

  @type system_blueprint :: %{
          id: String.t(),
          name: String.t(),
          description: String.t(),
          components: [atom()],
          connections: [{atom(), atom(), String.t()}],
          parameters: map(),
          novelty_score: float(),
          predicted_effectiveness: float(),
          generation_strategy: generation_strategy()
        }

  @type t :: %__MODULE__{
          signature: Dspy.Signature.t(),
          examples: [Dspy.Example.t()],
          max_retries: non_neg_integer(),
          generation_strategies: [generation_strategy()],
          combination_patterns: [atom()],
          evolution_parameters: map(),
          training_data_store: atom(),
          novelty_threshold: float(),
          success_criteria: map(),
          experiment_id: String.t()
        }

  def new(signature, opts \\ []) do
    base_signature = get_signature(signature)

    %__MODULE__{
      signature: base_signature,
      examples: Keyword.get(opts, :examples, []),
      max_retries: Keyword.get(opts, :max_retries, 3),
      generation_strategies: Keyword.get(opts, :generation_strategies, all_strategies()),
      combination_patterns: Keyword.get(opts, :combination_patterns, default_patterns()),
      evolution_parameters: Keyword.get(opts, :evolution_parameters, default_evolution_params()),
      training_data_store: Keyword.get(opts, :training_data_store, Dspy.TrainingDataStorage),
      novelty_threshold: Keyword.get(opts, :novelty_threshold, 0.6),
      success_criteria: Keyword.get(opts, :success_criteria, default_success_criteria()),
      experiment_id: Keyword.get(opts, :experiment_id, generate_experiment_id())
    }
  end

  @impl true
  def forward(generator, inputs) do
    with :ok <- Dspy.Signature.validate_inputs(generator.signature, inputs),
         {:ok, problem_analysis} <- analyze_problem_characteristics(generator, inputs),
         {:ok, novel_blueprints} <- generate_novel_systems(generator, inputs, problem_analysis),
         {:ok, evaluated_blueprints} <-
           evaluate_system_blueprints(generator, inputs, novel_blueprints),
         {:ok, best_system} <- select_best_system(evaluated_blueprints),
         {:ok, instantiated_system} <- instantiate_novel_system(generator, best_system),
         {:ok, result} <- execute_novel_system(instantiated_system, inputs),
         :ok <- store_experiment_data(generator, inputs, novel_blueprints, result) do
      enhanced_result =
        result
        |> Map.put(:novel_system_used, best_system.name)
        |> Map.put(:novelty_score, best_system.novelty_score)
        |> Map.put(:experiment_id, generator.experiment_id)

      prediction = Dspy.Prediction.new(enhanced_result)
      {:ok, prediction}
    else
      {:error, reason} -> {:error, reason}
    end
  end

  defp get_signature(signature) when is_atom(signature) do
    signature.signature()
  end

  defp get_signature(signature), do: signature

  defp analyze_problem_characteristics(generator, inputs) do
    analysis_signature = create_problem_analysis_signature(generator.signature)

    with {:ok, prompt} <- build_prompt(analysis_signature, inputs, generator.examples),
         {:ok, response} <- generate_with_retries(prompt, generator.max_retries),
         {:ok, analysis} <- parse_response(analysis_signature, response) do
      enhanced_analysis = %{
        complexity_level: Map.get(analysis, :complexity_level, "medium"),
        domain_type: Map.get(analysis, :domain_type, "general"),
        reasoning_requirements:
          parse_requirements_list(Map.get(analysis, :reasoning_requirements, "")),
        constraint_types: parse_constraints_list(Map.get(analysis, :constraint_types, "")),
        uncertainty_level: Map.get(analysis, :uncertainty_level, 0.5),
        multi_step_nature: Map.get(analysis, :multi_step_nature, true),
        creative_aspects: parse_creative_aspects(Map.get(analysis, :creative_aspects, "")),
        time_sensitivity: Map.get(analysis, :time_sensitivity, "medium")
      }

      {:ok, enhanced_analysis}
    else
      {:error, reason} -> {:error, reason}
    end
  end

  defp generate_novel_systems(generator, inputs, problem_analysis) do
    generation_tasks =
      generator.generation_strategies
      |> Enum.map(fn strategy ->
        Task.async(fn ->
          generate_system_with_strategy(generator, inputs, problem_analysis, strategy)
        end)
      end)

    strategy_results = Task.await_many(generation_tasks, 30_000)

    all_blueprints =
      strategy_results
      |> Enum.filter(fn
        {:ok, _} -> true
        _ -> false
      end)
      |> Enum.flat_map(fn {:ok, blueprints} -> blueprints end)
      |> Enum.filter(&(&1.novelty_score >= generator.novelty_threshold))

    if length(all_blueprints) > 0 do
      {:ok, all_blueprints}
    else
      # If no novel systems generated, create fallback systems
      {:ok, generate_fallback_systems(generator, problem_analysis)}
    end
  end

  defp generate_system_with_strategy(generator, inputs, problem_analysis, strategy) do
    case strategy do
      :hybrid_combination ->
        generate_hybrid_systems(generator, inputs, problem_analysis)

      :pattern_evolution ->
        generate_evolved_systems(generator, inputs, problem_analysis)

      :meta_reasoning ->
        generate_meta_reasoning_systems(generator, inputs, problem_analysis)

      :constraint_driven ->
        generate_constraint_driven_systems(generator, inputs, problem_analysis)

      :goal_decomposition ->
        generate_goal_decomposition_systems(generator, inputs, problem_analysis)

      :emergent_behavior ->
        generate_emergent_behavior_systems(generator, inputs, problem_analysis)

      _ ->
        {:ok, []}
    end
  end

  defp generate_hybrid_systems(generator, _inputs, problem_analysis) do
    existing_modules = [
      :adaptive_backtracking,
      :backward_chaining,
      :verification_behavior,
      :self_consistency,
      :multi_step,
      :reflection,
      :program_of_thoughts,
      :self_correcting_cot,
      :tree_of_thoughts
    ]

    # Generate combinations of 2-3 existing modules
    combinations = generate_module_combinations(existing_modules, 2, 3)

    blueprints =
      combinations
      # Limit to 5 combinations
      |> Enum.take(5)
      |> Enum.map(fn combo ->
        create_hybrid_blueprint(combo, problem_analysis, generator)
      end)

    {:ok, blueprints}
  end

  defp generate_evolved_systems(_generator, _inputs, problem_analysis) do
    # Create evolved versions of existing patterns
    base_patterns = [:chain_of_thought, :tree_search, :verification]

    blueprints =
      base_patterns
      |> Enum.map(fn pattern ->
        create_evolved_blueprint(pattern, problem_analysis)
      end)

    {:ok, blueprints}
  end

  defp generate_meta_reasoning_systems(_generator, _inputs, problem_analysis) do
    # Systems that reason about their own reasoning
    meta_approaches = [
      :reasoning_strategy_selection,
      :adaptive_depth_control,
      :confidence_guided_branching,
      :failure_pattern_learning
    ]

    blueprints =
      meta_approaches
      |> Enum.map(fn approach ->
        create_meta_reasoning_blueprint(approach, problem_analysis)
      end)

    {:ok, blueprints}
  end

  defp generate_constraint_driven_systems(_generator, _inputs, problem_analysis) do
    # Systems driven by problem constraints
    constraint_patterns = [
      :resource_constrained_reasoning,
      :time_bounded_exploration,
      :accuracy_optimized_search,
      :multi_objective_balancing
    ]

    blueprints =
      constraint_patterns
      |> Enum.map(fn pattern ->
        create_constraint_driven_blueprint(pattern, problem_analysis)
      end)

    {:ok, blueprints}
  end

  defp generate_goal_decomposition_systems(_generator, _inputs, problem_analysis) do
    # Novel goal decomposition approaches
    decomposition_strategies = [
      :hierarchical_goal_networks,
      :parallel_subgoal_pursuit,
      :goal_dependency_analysis,
      :adaptive_goal_refinement
    ]

    blueprints =
      decomposition_strategies
      |> Enum.map(fn strategy ->
        create_goal_decomposition_blueprint(strategy, problem_analysis)
      end)

    {:ok, blueprints}
  end

  defp generate_emergent_behavior_systems(_generator, _inputs, problem_analysis) do
    # Systems with emergent reasoning properties
    emergent_patterns = [
      :collaborative_reasoning_agents,
      :competitive_solution_evolution,
      :swarm_intelligence_reasoning,
      :evolutionary_approach_selection
    ]

    blueprints =
      emergent_patterns
      |> Enum.map(fn pattern ->
        create_emergent_behavior_blueprint(pattern, problem_analysis)
      end)

    {:ok, blueprints}
  end

  defp create_hybrid_blueprint(modules, problem_analysis, _generator) do
    module_names = Enum.join(modules, "_")

    %{
      id: generate_system_id(),
      name: "Hybrid_#{module_names}",
      description: "Hybrid system combining #{Enum.join(modules, ", ")}",
      components: modules,
      connections: generate_connections(modules),
      parameters: %{
        combination_strategy: :sequential,
        confidence_aggregation: :weighted_average,
        failure_handling: :graceful_degradation
      },
      novelty_score: calculate_novelty_score(modules, :hybrid),
      predicted_effectiveness: predict_effectiveness(modules, problem_analysis),
      generation_strategy: :hybrid_combination
    }
  end

  defp create_evolved_blueprint(base_pattern, problem_analysis) do
    enhancements = [:adaptive_parameters, :meta_learning, :context_awareness]

    %{
      id: generate_system_id(),
      name: "Evolved_#{base_pattern}",
      description: "Evolved version of #{base_pattern} with #{Enum.join(enhancements, ", ")}",
      components: [base_pattern | enhancements],
      connections: generate_evolution_connections(base_pattern, enhancements),
      parameters: %{
        adaptation_rate: 0.1,
        memory_depth: 10,
        learning_threshold: 0.7
      },
      novelty_score: calculate_novelty_score([base_pattern], :evolution),
      predicted_effectiveness: predict_effectiveness([base_pattern], problem_analysis) * 1.2,
      generation_strategy: :pattern_evolution
    }
  end

  defp create_meta_reasoning_blueprint(approach, problem_analysis) do
    %{
      id: generate_system_id(),
      name: "MetaReasoning_#{approach}",
      description: "Meta-reasoning system using #{approach}",
      components: [:meta_controller, approach, :performance_monitor],
      connections: [
        {:meta_controller, approach, "strategy_selection"},
        {approach, :performance_monitor, "outcome_feedback"},
        {:performance_monitor, :meta_controller, "adaptation_signal"}
      ],
      parameters: %{
        strategy_switching_threshold: 0.6,
        performance_window: 5,
        exploration_probability: 0.2
      },
      novelty_score: calculate_novelty_score([:meta_controller], :meta),
      predicted_effectiveness: predict_meta_effectiveness(approach, problem_analysis),
      generation_strategy: :meta_reasoning
    }
  end

  defp create_constraint_driven_blueprint(pattern, problem_analysis) do
    %{
      id: generate_system_id(),
      name: "ConstraintDriven_#{pattern}",
      description: "Constraint-driven system using #{pattern}",
      components: [:constraint_analyzer, pattern, :resource_monitor],
      connections: [
        {:constraint_analyzer, pattern, "constraint_guidance"},
        {:resource_monitor, pattern, "resource_limits"},
        {pattern, :constraint_analyzer, "feasibility_feedback"}
      ],
      parameters: %{
        constraint_strictness: 0.8,
        resource_budget: 1.0,
        optimization_target: :balanced
      },
      novelty_score: calculate_novelty_score([:constraint_analyzer], :constraint),
      predicted_effectiveness: predict_constraint_effectiveness(pattern, problem_analysis),
      generation_strategy: :constraint_driven
    }
  end

  defp create_goal_decomposition_blueprint(strategy, problem_analysis) do
    %{
      id: generate_system_id(),
      name: "GoalDecomposition_#{strategy}",
      description: "Goal decomposition system using #{strategy}",
      components: [:goal_analyzer, strategy, :subgoal_coordinator],
      connections: [
        {:goal_analyzer, strategy, "decomposition_strategy"},
        {strategy, :subgoal_coordinator, "subgoal_dependencies"},
        {:subgoal_coordinator, :goal_analyzer, "progress_feedback"}
      ],
      parameters: %{
        decomposition_depth: 4,
        parallel_execution: true,
        dependency_resolution: :topological
      },
      novelty_score: calculate_novelty_score([:goal_analyzer], :goal_decomposition),
      predicted_effectiveness: predict_goal_effectiveness(strategy, problem_analysis),
      generation_strategy: :goal_decomposition
    }
  end

  defp create_emergent_behavior_blueprint(pattern, problem_analysis) do
    %{
      id: generate_system_id(),
      name: "Emergent_#{pattern}",
      description: "Emergent behavior system using #{pattern}",
      components: [:agent_pool, pattern, :emergence_detector],
      connections: [
        {:agent_pool, pattern, "collective_reasoning"},
        {pattern, :emergence_detector, "behavior_patterns"},
        {:emergence_detector, :agent_pool, "adaptation_signals"}
      ],
      parameters: %{
        agent_count: 5,
        interaction_probability: 0.3,
        emergence_threshold: 0.6
      },
      novelty_score: calculate_novelty_score([:agent_pool], :emergent),
      predicted_effectiveness: predict_emergent_effectiveness(pattern, problem_analysis),
      generation_strategy: :emergent_behavior
    }
  end

  defp generate_fallback_systems(_generator, _problem_analysis) do
    # Simple fallback systems when novel generation fails
    [
      %{
        id: generate_system_id(),
        name: "Fallback_Enhanced_CoT",
        description: "Enhanced chain of thought with problem-specific adaptations",
        components: [:chain_of_thought, :verification],
        connections: [{:chain_of_thought, :verification, "step_validation"}],
        parameters: %{verification_frequency: 2},
        novelty_score: 0.3,
        predicted_effectiveness: 0.7,
        generation_strategy: :fallback
      }
    ]
  end

  # Helper functions for blueprint creation

  defp generate_module_combinations(modules, min_size, max_size) do
    min_size..max_size
    |> Enum.flat_map(fn size ->
      combinations(modules, size)
    end)
  end

  defp combinations([], _), do: [[]]
  defp combinations(_, 0), do: [[]]

  defp combinations([h | t], n) when n > 0 do
    for(combo <- combinations(t, n - 1), do: [h | combo]) ++ combinations(t, n)
  end

  defp generate_connections(modules) do
    # Simple sequential connections for now
    modules
    |> Enum.chunk_every(2, 1, :discard)
    |> Enum.map(fn [a, b] -> {a, b, "sequential"} end)
  end

  defp generate_evolution_connections(base_pattern, enhancements) do
    enhancements
    |> Enum.map(fn enhancement ->
      {enhancement, base_pattern, "enhancement"}
    end)
  end

  defp calculate_novelty_score(components, strategy) do
    base_score =
      case strategy do
        :hybrid -> 0.6
        :evolution -> 0.7
        :meta -> 0.8
        :constraint -> 0.6
        :goal_decomposition -> 0.7
        :emergent -> 0.9
        _ -> 0.4
      end

    # Adjust based on component uniqueness
    uniqueness_bonus = length(components) * 0.05

    min(1.0, base_score + uniqueness_bonus)
  end

  defp predict_effectiveness(components, problem_analysis) do
    # Simple heuristic-based prediction
    base_effectiveness = 0.6

    complexity_match =
      case problem_analysis.complexity_level do
        "high" when length(components) > 2 -> 0.2
        "medium" when length(components) == 2 -> 0.15
        "low" when length(components) == 1 -> 0.1
        _ -> 0.0
      end

    min(1.0, base_effectiveness + complexity_match)
  end

  defp predict_meta_effectiveness(_approach, problem_analysis) do
    base = 0.75
    if problem_analysis.uncertainty_level > 0.7, do: base + 0.15, else: base
  end

  defp predict_constraint_effectiveness(_pattern, problem_analysis) do
    base = 0.7
    if length(problem_analysis.constraint_types) > 2, do: base + 0.2, else: base
  end

  defp predict_goal_effectiveness(_strategy, problem_analysis) do
    base = 0.7
    if problem_analysis.multi_step_nature, do: base + 0.15, else: base
  end

  defp predict_emergent_effectiveness(_pattern, problem_analysis) do
    base = 0.65
    if "creative" in problem_analysis.creative_aspects, do: base + 0.25, else: base
  end

  # System evaluation and execution

  defp evaluate_system_blueprints(_generator, _inputs, blueprints) do
    # Quick evaluation to rank blueprints
    evaluated =
      blueprints
      |> Enum.map(fn blueprint ->
        score = blueprint.novelty_score * 0.4 + blueprint.predicted_effectiveness * 0.6
        Map.put(blueprint, :evaluation_score, score)
      end)
      |> Enum.sort_by(& &1.evaluation_score, :desc)

    {:ok, evaluated}
  end

  defp select_best_system(evaluated_blueprints) do
    case evaluated_blueprints do
      [] -> {:error, :no_systems_generated}
      [best | _] -> {:ok, best}
    end
  end

  defp instantiate_novel_system(_generator, blueprint) do
    # For now, return a simplified system representation
    system = %{
      blueprint: blueprint,
      execute: fn _inputs ->
        # Placeholder execution logic
        {:ok,
         %{
           reasoning:
             "Novel system #{blueprint.name} processed the problem using #{Enum.join(blueprint.components, ", ")}",
           confidence: blueprint.predicted_effectiveness,
           novel_insights: [
             "Applied #{blueprint.generation_strategy}",
             "Used #{length(blueprint.components)} components"
           ]
         }}
      end
    }

    {:ok, system}
  end

  defp execute_novel_system(system, inputs) do
    system.execute.(inputs)
  end

  defp store_experiment_data(generator, inputs, blueprints, result) do
    experiment_data = %{
      experiment_id: generator.experiment_id,
      timestamp: DateTime.utc_now(),
      inputs: inputs,
      generated_blueprints: blueprints,
      selected_system: result[:novel_system_used],
      result: result,
      success: determine_success(result, generator.success_criteria)
    }

    # Store in the training data storage
    if Code.ensure_loaded?(generator.training_data_store) do
      generator.training_data_store.store_experiment(experiment_data)
    end

    :ok
  end

  # Utility functions

  defp all_strategies do
    [
      :hybrid_combination,
      :pattern_evolution,
      :meta_reasoning,
      :constraint_driven,
      :goal_decomposition,
      :emergent_behavior
    ]
  end

  defp default_patterns do
    [:sequential, :parallel, :hierarchical, :recursive]
  end

  defp default_evolution_params do
    %{
      mutation_rate: 0.1,
      selection_pressure: 0.7,
      diversity_preservation: 0.3
    }
  end

  defp default_success_criteria do
    %{
      min_confidence: 0.6,
      max_time: 30_000,
      min_novelty: 0.5
    }
  end

  defp generate_experiment_id do
    "exp_#{System.unique_integer([:positive])}_#{:rand.uniform(10000)}"
  end

  defp generate_system_id do
    "sys_#{System.unique_integer([:positive])}_#{:rand.uniform(1000)}"
  end

  defp determine_success(result, criteria) do
    confidence_ok = Map.get(result, :confidence, 0) >= criteria.min_confidence
    novelty_ok = Map.get(result, :novelty_score, 0) >= criteria.min_novelty

    confidence_ok and novelty_ok
  end

  # Parsing helpers

  defp parse_requirements_list(text) do
    parse_list(text)
  end

  defp parse_constraints_list(text) do
    parse_list(text)
  end

  defp parse_creative_aspects(text) do
    parse_list(text)
  end

  defp parse_list(text) do
    if is_binary(text) and String.trim(text) != "" do
      text
      |> String.split(~r/[,;.\n]/)
      |> Enum.map(&String.trim/1)
      |> Enum.reject(&(&1 == ""))
    else
      []
    end
  end

  # Signature creation

  defp create_problem_analysis_signature(base_signature) do
    output_fields = [
      %{
        name: :complexity_level,
        type: :string,
        description: "Problem complexity: low, medium, high",
        required: true,
        default: nil
      },
      %{
        name: :domain_type,
        type: :string,
        description: "Domain type: mathematical, logical, creative, analytical",
        required: true,
        default: nil
      },
      %{
        name: :reasoning_requirements,
        type: :string,
        description: "Required reasoning types (comma-separated)",
        required: true,
        default: nil
      },
      %{
        name: :constraint_types,
        type: :string,
        description: "Types of constraints present (comma-separated)",
        required: false,
        default: nil
      },
      %{
        name: :uncertainty_level,
        type: :number,
        description: "Level of uncertainty (0-1)",
        required: false,
        default: nil
      },
      %{
        name: :multi_step_nature,
        type: :boolean,
        description: "Whether problem requires multiple steps",
        required: false,
        default: nil
      },
      %{
        name: :creative_aspects,
        type: :string,
        description: "Creative aspects needed (comma-separated)",
        required: false,
        default: nil
      },
      %{
        name: :time_sensitivity,
        type: :string,
        description: "Time sensitivity: low, medium, high",
        required: false,
        default: nil
      }
    ]

    instructions = """
    Analyze the problem to understand its characteristics and requirements.
    Identify what types of reasoning, constraints, and approaches would be most suitable.
    Consider complexity, domain, uncertainty, and creative requirements.
    """

    %{base_signature | output_fields: output_fields, instructions: instructions}
  end

  defp build_prompt(signature, inputs, examples) do
    prompt_template = Dspy.Signature.to_prompt(signature, examples)

    filled_prompt =
      Enum.reduce(inputs, prompt_template, fn {key, value}, acc ->
        placeholder = "[input]"
        field_name = String.capitalize(Atom.to_string(key))
        String.replace(acc, "#{field_name}: #{placeholder}", "#{field_name}: #{value}")
      end)

    {:ok, filled_prompt}
  end

  defp generate_with_retries(prompt, retries) do
    case Dspy.LM.generate_text(prompt) do
      {:ok, response} ->
        {:ok, response}

      {:error, _reason} when retries > 0 ->
        Process.sleep(1000)
        generate_with_retries(prompt, retries - 1)

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

  defp parse_response(signature, response_text) do
    outputs = Dspy.Signature.parse_outputs(signature, response_text)
    {:ok, outputs}
  end
end
