# Week 1: Define the foundational types
defmodule JsonRemedy.Core.Types do
  @type json_violation :: %{
    type: atom(),
    position: non_neg_integer(),
    severity: 1..10,
    context: map(),
    suggested_fixes: [repair_hypothesis()]
  }
  
  @type repair_hypothesis :: %{
    action: atom(),
    position: non_neg_integer(),
    replacement: String.t() | nil,
    simplicity_score: float(),
    confidence: float(),
    reasoning: String.t()
  }
  
  @type transformation_rule :: %{
    pattern: Regex.t() | binary(),
    conditions: [condition()],
    transform: function(),
    confidence: float(),
    examples: [example()],
    usage_count: non_neg_integer(),
    success_rate: float()
  }
end

# Week 1-2: Build the structural analyzer
defmodule JsonRemedy.StructuralAnalyzer do
  @doc "Analyze intended JSON structure without parsing"
  def analyze_structure(input) do
    %{
      bracket_analysis: analyze_brackets(input),
      delimiter_analysis: analyze_delimiters(input), 
      quote_analysis: analyze_quotes(input),
      nesting_analysis: analyze_nesting(input),
      token_analysis: analyze_tokens(input)
    }
  end
  
  # Tactic: Start with simple, reliable detection
  defp analyze_brackets(input) do
    # Step-by-step bracket matching with position tracking
    # Track: opening position, expected closing, actual closing
  end
  
  defp analyze_delimiters(input) do
    # Comma and colon placement analysis
    # Detect: missing commas, trailing commas, misplaced colons
  end
  
  defp analyze_quotes(input) do
    # Quote pairing and string boundary detection
    # Handle: escaped quotes, mixed quote types, unclosed strings
  end
end

# Week 2: Build violation detectors
defmodule JsonRemedy.ViolationDetector do
  @detectors [
    {JsonRemedy.Detectors.BracketMismatch, priority: 1},
    {JsonRemedy.Detectors.QuoteMismatch, priority: 2},
    {JsonRemedy.Detectors.CommaIssues, priority: 3},
    {JsonRemedy.Detectors.InvalidLiterals, priority: 4},
    {JsonRemedy.Detectors.ContextMismatch, priority: 5}
  ]
  
  def detect_all_violations(input) do
    @detectors
    |> Enum.flat_map(fn {detector, priority} ->
      detector.detect(input)
      |> Enum.map(&Map.put(&1, :detector_priority, priority))
    end)
    |> Enum.sort_by(&{&1.detector_priority, &1.severity})
  end
end

# Week 3: Build hypothesis generators
defmodule JsonRemedy.HypothesisGenerator do
  def generate_for_violation(violation) do
    case violation.type do
      :missing_comma -> generate_comma_hypotheses(violation)
      :unmatched_quote -> generate_quote_hypotheses(violation)
      :invalid_literal -> generate_literal_hypotheses(violation)
      :bracket_mismatch -> generate_bracket_hypotheses(violation)
    end
  end
  
  # Tactic: Start with simplest, most reliable hypotheses
  defp generate_comma_hypotheses(violation) do
    [
      %Hypothesis{
        action: :insert_comma,
        position: violation.position,
        simplicity_score: 1.0,
        confidence: calculate_comma_confidence(violation),
        reasoning: "Missing comma between values"
      },
      # Add more sophisticated hypotheses
    ]
  end
end

# Week 3-4: Build hypothesis testing
defmodule JsonRemedy.HypothesisTester do
  def test_hypothesis(input, hypothesis) do
    case apply_hypothesis(input, hypothesis) do
      {:ok, repaired_input} ->
        validation_result = validate_json(repaired_input)
        confidence_score = calculate_confidence(hypothesis, validation_result)
        
        {:ok, repaired_input, confidence_score}
        
      {:error, reason} ->
        {:error, reason}
    end
  end
  
  defp apply_hypothesis(input, hypothesis) do
    case hypothesis.action do
      :insert_comma -> insert_at_position(input, hypothesis.position, ",")
      :close_quote -> insert_at_position(input, hypothesis.position, "\"")
      :normalize_literal -> replace_at_position(input, hypothesis.position, hypothesis.replacement)
    end
  end
  
  # Tactic: Fast validation without full JSON parsing
  defp validate_json(input) do
    case Jason.decode(input) do
      {:ok, _} -> {:valid, 1.0}
      {:error, %Jason.DecodeError{} = error} -> 
        {:invalid, analyze_parse_error(error)}
    end
  end
end

# Week 4: Build compositional repair system
defmodule JsonRemedy.CompositionEngine do
  def repair_compositionally(input, max_iterations \\ 5) do
    repair_compositionally(input, [], 1.0, 0, max_iterations)
  end
  
  defp repair_compositionally(input, applied_repairs, confidence, iteration, max_iter) 
       when iteration >= max_iter do
    {:max_iterations, input, applied_repairs, confidence}
  end
  
  defp repair_compositionally(input, applied_repairs, confidence, iteration, max_iter) do
    case validate_json(input) do
      {:valid, _} -> 
        {:ok, input, applied_repairs, confidence}
        
      {:invalid, _} ->
        case find_and_apply_best_repair(input) do
          {:ok, new_input, repair, new_confidence} ->
            repair_compositionally(
              new_input, 
              [repair | applied_repairs], 
              confidence * new_confidence,
              iteration + 1,
              max_iter
            )
            
          {:no_repair_found} ->
            {:error, input, applied_repairs, confidence}
        end
    end
  end
end

# Week 5: Build pattern learning
defmodule JsonRemedy.PatternLearner do
  def learn_from_successful_repair(original_input, repaired_input, applied_repairs) do
    # Extract generalizable patterns from successful repairs
    transformation_pattern = extract_transformation_pattern(
      original_input, 
      repaired_input, 
      applied_repairs
    )
    
    # Test if pattern generalizes
    generalization_score = test_generalization(transformation_pattern)
    
    if generalization_score > 0.7 do
      PatternDatabase.add_pattern(transformation_pattern, generalization_score)
    end
  end
  
  defp extract_transformation_pattern(original, repaired, repairs) do
    # Find the minimal edit sequence
    edits = compute_edit_sequence(original, repaired)
    
    # Abstract specific characters to pattern variables
    abstract_pattern = abstract_edits_to_pattern(edits)
    
    # Generate applicability conditions
    conditions = infer_conditions_from_context(original, repairs)
    
    %TransformationRule{
      pattern: abstract_pattern,
      conditions: conditions,
      confidence: calculate_pattern_confidence(edits, repairs),
      examples: [{original, repaired, repairs}]
    }
  end
end

# Week 5-6: Build discovery system
defmodule JsonRemedy.PatternDiscovery do
  def discover_patterns_in_real_time(input) do
    # Analyze input for structural patterns
    structural_analysis = analyze_structural_patterns(input)
    
    # Generate repair hypotheses based on patterns
    pattern_based_hypotheses = generate_pattern_hypotheses(structural_analysis)
    
    # Test hypotheses and learn from results
    tested_hypotheses = test_and_learn_from_hypotheses(input, pattern_based_hypotheses)
    
    select_best_hypothesis(tested_hypotheses)
  end
  
  defp analyze_structural_patterns(input) do
    %{
      repeating_patterns: find_repeating_patterns(input),
      symmetry_violations: find_symmetry_violations(input),
      context_switches: find_context_switches(input),
      boundary_patterns: find_boundary_patterns(input)
    }
  end
end

# Week 6: Build knowledge management
defmodule JsonRemedy.KnowledgeBase do
  use GenServer
  
  # Tactic: In-memory knowledge base with persistence
  def start_link(opts \\ []) do
    GenServer.start_link(__MODULE__, opts, name: __MODULE__)
  end
  
  def add_pattern(pattern, confidence) do
    GenServer.call(__MODULE__, {:add_pattern, pattern, confidence})
  end
  
  def get_applicable_patterns(input_context) do
    GenServer.call(__MODULE__, {:get_patterns, input_context})
  end
  
  def update_pattern_success(pattern_id, success) do
    GenServer.cast(__MODULE__, {:update_success, pattern_id, success})
  end
  
  # Handle state with automatic cleanup of low-performing patterns
  def handle_call({:add_pattern, pattern, confidence}, _from, state) do
    updated_patterns = add_pattern_to_state(state.patterns, pattern, confidence)
    cleaned_patterns = cleanup_low_performers(updated_patterns)
    
    {:reply, :ok, %{state | patterns: cleaned_patterns}}
  end
end

# Week 7: Build structural reasoning
defmodule JsonRemedy.StructuralReasoner do
  @doc "Reason about JSON structure without training examples"
  def reason_about_structure(input) do
    # Analyze what JSON structure this could be
    possible_structures = enumerate_possible_structures(input)
    
    # Score each structure by likelihood
    scored_structures = score_structural_likelihood(possible_structures, input)
    
    # Generate repair plans for most likely structures
    repair_plans = generate_structural_repair_plans(scored_structures)
    
    select_optimal_repair_plan(repair_plans)
  end
  
  defp enumerate_possible_structures(input) do
    # Tactic: Use bracket/quote patterns to infer possible structures
    bracket_patterns = extract_bracket_patterns(input)
    quote_patterns = extract_quote_patterns(input)
    
    # Generate structural hypotheses
    generate_structural_hypotheses(bracket_patterns, quote_patterns)
  end
  
  defp score_structural_likelihood(structures, input) do
    Enum.map(structures, fn structure ->
      score = calculate_structure_score(structure, input)
      {structure, score}
    end)
    |> Enum.sort_by(&elem(&1, 1), :desc)
  end
end

# Week 7-8: Build novel pattern handling
defmodule JsonRemedy.NovelPatternHandler do
  def handle_novel_pattern(input, failed_repairs \\ []) do
    # Step 1: Identify why existing patterns failed
    failure_analysis = analyze_repair_failures(input, failed_repairs)
    
    # Step 2: Generate novel hypotheses based on failure analysis
    novel_hypotheses = generate_novel_hypotheses(input, failure_analysis)
    
    # Step 3: Test novel hypotheses with careful validation
    test_novel_hypotheses(input, novel_hypotheses)
  end
  
  defp generate_novel_hypotheses(input, failure_analysis) do
    # Tactic: Use failure modes to guide novel hypothesis generation
    failure_analysis.failure_modes
    |> Enum.flat_map(&generate_hypotheses_for_failure_mode(&1, input))
    |> Enum.sort_by(& &1.novelty_score)
  end
  
  defp test_novel_hypotheses(input, hypotheses) do
    # Careful testing with multiple validation steps
    Enum.find_value(hypotheses, fn hypothesis ->
      case test_hypothesis_thoroughly(input, hypothesis) do
        {:ok, result, confidence} when confidence > 0.6 ->
          # Learn from this novel pattern
          learn_novel_pattern(input, result, hypothesis)
          {:ok, result, confidence}
          
        _ ->
          nil
      end
    end)
  end
end

# Week 9: Build performance optimizations
defmodule JsonRemedy.PerformanceLayer do
  @doc "Fast path for common patterns"
  def fast_path_repair(input) do
    # Tactic: Use Elixir's binary pattern matching for O(1) common cases
    case input do
      # Pattern 1: Simple quote fixes
      <<prefix::binary, "'", content::binary, "'", suffix::binary>> 
        when not String.contains?(content, "'") ->
        {:fast_repair, prefix <> "\"" <> content <> "\"" <> suffix}
      
      # Pattern 2: Python literal fixes
      input when binary_contains_python_literals(input) ->
        {:fast_repair, normalize_python_literals(input)}
      
      # Pattern 3: Trailing comma fixes
      input when has_trailing_commas(input) ->
        {:fast_repair, remove_trailing_commas(input)}
      
      # Fall back to full analysis
      _ ->
        {:needs_analysis, input}
    end
  end
  
  defp binary_contains_python_literals(input) do
    String.contains?(input, "True") or 
    String.contains?(input, "False") or 
    String.contains?(input, "None")
  end
end

# Week 9-10: Integrate with existing system
defmodule JsonRemedy.EnhancedLayer3 do
  @behaviour JsonRemedy.LayerBehaviour
  
  def process(input, context) do
    # Try fast path first
    case PerformanceLayer.fast_path_repair(input) do
      {:fast_repair, repaired} ->
        {:ok, repaired, update_context(context, :fast_path)}
      
      {:needs_analysis, input} ->
        # Use full compositional repair system
        case CompositionEngine.repair_compositionally(input) do
          {:ok, repaired, repairs, confidence} ->
            # Learn from this repair
            PatternLearner.learn_from_successful_repair(input, repaired, repairs)
            {:ok, repaired, update_context(context, {:compositional, repairs, confidence})}
          
          {:error, _, _, _} ->
            # Try novel pattern handling
            NovelPatternHandler.handle_novel_pattern(input)
        end
    end
  end
end

# Week 10: Build monitoring system
defmodule JsonRemedy.Metrics do
  def track_repair_attempt(input, result, method, duration) do
    :telemetry.execute(
      [:json_remedy, :repair_attempt],
      %{duration: duration, input_size: byte_size(input)},
      %{method: method, success: success?(result)}
    )
    
    # Update pattern performance metrics
    update_pattern_metrics(method, result)
  end
  
  def get_performance_summary do
    %{
      fast_path_hit_rate: calculate_fast_path_hit_rate(),
      average_repair_time: calculate_average_repair_time(),
      pattern_success_rates: get_pattern_success_rates(),
      novel_pattern_discovery_rate: get_discovery_rate()
    }
  end
end