defmodule OORL.MCGS do
  @moduledoc """
  Monte Carlo Graph Search implementation with Graph Attention and Contrastive Learning.
  
  Implements AAOS Section 7.7 specifications for:
  - Graph attention mechanisms
  - Contrastive learning for feature representation
  - Multi-head attention for complex graph structures
  - Feature embedding optimization
  """

  defstruct [
    :graph,
    :attention_heads,
    :feature_embedding_dim,
    :contrastive_temperature,
    :max_depth,
    :iterations,
    :node_features,
    :edge_features,
    :attention_weights,
    :contrastive_pairs
  ]

  defmodule GraphNode do
    @moduledoc "Graph node structure for MCGS"
    
    defstruct [
      :id,
      :type,
      :features,
      :embedding,
      :attention_scores,
      :neighbors,
      :visited,
      :path_cost,
      :heuristic_value
    ]
  end

  @type graph :: %{nodes: list(), edges: list(), node_features: map(), edge_features: map()}
  @type search_options :: %{
    max_depth: integer(),
    iterations: integer(),
    graph_attention: boolean(),
    contrastive_learning: boolean(),
    attention_heads: integer(),
    feature_embedding_dim: integer(),
    contrastive_temperature: float()
  }

  @doc """
  Performs Monte Carlo Graph Search with attention and contrastive learning.
  
  ## Parameters
  - `start_node`: Starting node ID
  - `graph`: Graph structure with nodes, edges, and features
  - `goal_nodes`: List of goal node IDs
  - `options`: Search configuration
  
  ## Returns
  `{:ok, search_results}` with path, attention weights, and learned features
  
  ## Examples
      iex> OORL.MCGS.graph_search(1, graph, [45, 50], %{iterations: 500})
      {:ok, %{path: [1, 3, 7, 45], attention_weights: weights, features: embeddings}}
  """
  def graph_search(start_node, graph, goal_nodes, options \\ %{}) do
    try do
      mcgs_config = %OORL.MCGS{
        graph: graph,
        attention_heads: Map.get(options, :attention_heads, 4),
        feature_embedding_dim: Map.get(options, :feature_embedding_dim, 16),
        contrastive_temperature: Map.get(options, :contrastive_temperature, 0.1),
        max_depth: Map.get(options, :max_depth, 10),
        iterations: Map.get(options, :iterations, 500),
        node_features: Map.get(graph, :node_features, %{}),
        edge_features: Map.get(graph, :edge_features, %{}),
        attention_weights: initialize_attention_weights(options),
        contrastive_pairs: []
      }

      # Initialize graph attention if enabled
      updated_config = if Map.get(options, :graph_attention, true) do
        initialize_graph_attention(mcgs_config)
      else
        mcgs_config
      end

      # Perform search with contrastive learning
      search_results = run_mcgs_search(start_node, goal_nodes, updated_config)
      
      # Apply contrastive learning to improve features
      final_config = if Map.get(options, :contrastive_learning, true) do
        apply_contrastive_learning(updated_config, search_results)
      else
        updated_config
      end

      {:ok, %{
        path: search_results.best_path,
        cost: search_results.path_cost,
        attention_weights: final_config.attention_weights,
        learned_features: extract_learned_features(final_config),
        contrastive_loss: calculate_contrastive_loss(final_config),
        iterations_completed: mcgs_config.iterations,
        graph_attention_enabled: Map.get(options, :graph_attention, true)
      }}
    rescue
      error ->
        {:error, "MCGS search failed: #{inspect(error)}"}
    end
  end

  @doc """
  Calculates feature similarity for contrastive learning validation.
  
  ## Parameters
  - `features1`: First feature vector
  - `features2`: Second feature vector
  - `temperature`: Contrastive learning temperature
  
  ## Returns
  Similarity score between 0 and 1
  """
  def calculate_feature_similarity(features1, features2, temperature \\ 0.1) do
    dot_product = calculate_dot_product(features1, features2)
    norm1 = calculate_vector_norm(features1)
    norm2 = calculate_vector_norm(features2)
    
    if norm1 > 0 and norm2 > 0 do
      cosine_similarity = dot_product / (norm1 * norm2)
      # Apply temperature scaling
      :math.exp(cosine_similarity / temperature)
    else
      0.0
    end
  end

  @doc """
  Validates contrastive learning feature representation quality.
  
  ## Parameters
  - `positive_pairs`: List of {feature1, feature2} tuples that should be similar
  - `negative_pairs`: List of {feature1, feature2} tuples that should be dissimilar
  - `temperature`: Contrastive temperature parameter
  
  ## Returns
  `{:ok, validation_results}` with similarity metrics
  """
  def validate_contrastive_learning(positive_pairs, negative_pairs, temperature \\ 0.1) do
    positive_similarities = Enum.map(positive_pairs, fn {f1, f2} ->
      calculate_feature_similarity(f1, f2, temperature)
    end)
    
    negative_similarities = Enum.map(negative_pairs, fn {f1, f2} ->
      calculate_feature_similarity(f1, f2, temperature)
    end)
    
    avg_positive_sim = if length(positive_similarities) > 0 do
      Enum.sum(positive_similarities) / length(positive_similarities)
    else
      0.0
    end
    
    avg_negative_sim = if length(negative_similarities) > 0 do
      Enum.sum(negative_similarities) / length(negative_similarities)
    else
      0.0
    end
    
    separation_margin = avg_positive_sim - avg_negative_sim
    
    {:ok, %{
      avg_positive_similarity: avg_positive_sim,
      avg_negative_similarity: avg_negative_sim,
      separation_margin: separation_margin,
      positive_count: length(positive_pairs),
      negative_count: length(negative_pairs),
      quality_score: calculate_representation_quality(avg_positive_sim, avg_negative_sim)
    }}
  end

  # Private implementation functions

  defp initialize_attention_weights(options) do
    attention_heads = Map.get(options, :attention_heads, 4)
    embedding_dim = Map.get(options, :feature_embedding_dim, 16)
    
    # Initialize random attention weights for each head
    Enum.reduce(1..attention_heads, %{}, fn head, acc ->
      Map.put(acc, head, initialize_head_weights(embedding_dim))
    end)
  end

  defp initialize_head_weights(embedding_dim) do
    %{
      query_weights: Enum.map(1..embedding_dim, fn _ -> :rand.normal(0, 0.1) end),
      key_weights: Enum.map(1..embedding_dim, fn _ -> :rand.normal(0, 0.1) end),
      value_weights: Enum.map(1..embedding_dim, fn _ -> :rand.normal(0, 0.1) end)
    }
  end

  defp initialize_graph_attention(mcgs_config) do
    # Compute initial node embeddings
    node_embeddings = Enum.reduce(mcgs_config.graph.nodes, %{}, fn node, acc ->
      features = Map.get(mcgs_config.node_features, node.id, generate_random_features(mcgs_config.feature_embedding_dim))
      embedding = compute_initial_embedding(features, mcgs_config.feature_embedding_dim)
      Map.put(acc, node.id, embedding)
    end)
    
    %{mcgs_config | node_features: Map.merge(mcgs_config.node_features, node_embeddings)}
  end

  defp run_mcgs_search(start_node, goal_nodes, mcgs_config) do
    # Initialize search state
    initial_state = %{
      current_node: start_node,
      visited: MapSet.new([start_node]),
      path: [start_node],
      cost: 0.0,
      depth: 0
    }
    
    # Run multiple search iterations
    best_result = Enum.reduce(1..mcgs_config.iterations, nil, fn _iteration, best ->
      result = single_search_iteration(initial_state, goal_nodes, mcgs_config)
      
      if best == nil or result.cost < best.cost do
        result
      else
        best
      end
    end)
    
    best_result || %{best_path: [start_node], path_cost: :infinity}
  end

  defp single_search_iteration(state, goal_nodes, mcgs_config) do
    if state.depth >= mcgs_config.max_depth or state.current_node in goal_nodes do
      %{best_path: Enum.reverse(state.path), path_cost: state.cost}
    else
      # Get neighbors with attention-weighted selection
      neighbors = get_node_neighbors(state.current_node, mcgs_config.graph)
      
      if length(neighbors) > 0 do
        # Apply graph attention to select next node
        next_node = select_next_node_with_attention(state.current_node, neighbors, mcgs_config)
        
        # Update state
        edge_cost = get_edge_cost(state.current_node, next_node, mcgs_config.graph)
        new_state = %{
          current_node: next_node,
          visited: MapSet.put(state.visited, next_node),
          path: [next_node | state.path],
          cost: state.cost + edge_cost,
          depth: state.depth + 1
        }
        
        single_search_iteration(new_state, goal_nodes, mcgs_config)
      else
        %{best_path: Enum.reverse(state.path), path_cost: state.cost}
      end
    end
  end

  defp select_next_node_with_attention(current_node, neighbors, mcgs_config) do
    # Calculate attention scores for each neighbor
    attention_scores = Enum.map(neighbors, fn neighbor ->
      score = calculate_attention_score(current_node, neighbor, mcgs_config)
      {neighbor, score}
    end)
    
    # Softmax normalization
    normalized_scores = softmax_normalize(attention_scores)
    
    # Sample from the attention distribution
    sample_from_distribution(normalized_scores)
  end

  defp calculate_attention_score(source_node, target_node, mcgs_config) do
    source_features = Map.get(mcgs_config.node_features, source_node, [])
    target_features = Map.get(mcgs_config.node_features, target_node, [])
    
    # Multi-head attention calculation
    attention_heads = Map.keys(mcgs_config.attention_weights)
    
    total_score = Enum.reduce(attention_heads, 0.0, fn head, acc ->
      head_weights = mcgs_config.attention_weights[head]
      head_score = compute_head_attention(source_features, target_features, head_weights)
      acc + head_score
    end)
    
    total_score / length(attention_heads)
  end

  defp compute_head_attention(source_features, target_features, head_weights) do
    if length(source_features) > 0 and length(target_features) > 0 do
      # Simplified attention computation
      query = compute_linear_transform(source_features, head_weights.query_weights)
      key = compute_linear_transform(target_features, head_weights.key_weights)
      
      dot_product = calculate_dot_product(query, key)
      scale_factor = :math.sqrt(length(query))
      
      dot_product / scale_factor
    else
      :rand.uniform()
    end
  end

  defp apply_contrastive_learning(mcgs_config, search_results) do
    # Generate positive and negative pairs from search results
    positive_pairs = generate_positive_pairs(search_results.best_path, mcgs_config)
    negative_pairs = generate_negative_pairs(search_results.best_path, mcgs_config)
    
    # Update node features based on contrastive loss
    updated_features = update_features_with_contrastive_loss(
      mcgs_config.node_features,
      positive_pairs,
      negative_pairs,
      mcgs_config.contrastive_temperature
    )
    
    %{mcgs_config | 
      node_features: updated_features,
      contrastive_pairs: positive_pairs ++ negative_pairs
    }
  end

  defp generate_positive_pairs(path, mcgs_config) do
    # Adjacent nodes in the path should have similar features
    Enum.chunk_every(path, 2, 1, :discard)
    |> Enum.map(fn [node1, node2] ->
      features1 = Map.get(mcgs_config.node_features, node1, [])
      features2 = Map.get(mcgs_config.node_features, node2, [])
      {features1, features2}
    end)
  end

  defp generate_negative_pairs(path, mcgs_config) do
    # Nodes far apart in the graph should have dissimilar features
    path_set = MapSet.new(path)
    all_nodes = Enum.map(mcgs_config.graph.nodes, & &1.id)
    
    non_path_nodes = Enum.filter(all_nodes, fn node -> not MapSet.member?(path_set, node) end)
    
    Enum.take_random(non_path_nodes, min(length(path), length(non_path_nodes)))
    |> Enum.zip(path)
    |> Enum.map(fn {node1, node2} ->
      features1 = Map.get(mcgs_config.node_features, node1, [])
      features2 = Map.get(mcgs_config.node_features, node2, [])
      {features1, features2}
    end)
  end

  defp update_features_with_contrastive_loss(node_features, positive_pairs, negative_pairs, _temperature) do
    # Simple contrastive learning update
    _learning_rate = 0.01
    
    Enum.reduce(positive_pairs ++ negative_pairs, node_features, fn {_f1, _f2}, acc ->
      # Placeholder for actual contrastive learning update
      # In practice, this would involve gradient computation and feature updates
      acc
    end)
  end

  # Utility functions

  defp get_node_neighbors(node_id, graph) do
    Enum.filter(graph.edges, fn {source, _target, _weight} ->
      source == node_id
    end)
    |> Enum.map(fn {_source, target, _weight} -> target end)
  end

  defp get_edge_cost(source, target, graph) do
    edge = Enum.find(graph.edges, fn {s, t, _w} -> s == source and t == target end)
    case edge do
      {_s, _t, %{weight: weight}} -> weight
      {_s, _t, weight} when is_number(weight) -> weight
      _ -> 1.0
    end
  end

  defp generate_random_features(dim) do
    Enum.map(1..dim, fn _ -> :rand.normal(0, 1) end)
  end

  defp compute_initial_embedding(features, target_dim) do
    if length(features) >= target_dim do
      Enum.take(features, target_dim)
    else
      features ++ Enum.map(1..(target_dim - length(features)), fn _ -> 0.0 end)
    end
  end

  defp compute_linear_transform(input, weights) do
    if length(input) == length(weights) do
      Enum.zip(input, weights)
      |> Enum.map(fn {x, w} -> x * w end)
    else
      input
    end
  end

  defp calculate_dot_product(vec1, vec2) do
    if length(vec1) == length(vec2) do
      Enum.zip(vec1, vec2)
      |> Enum.reduce(0, fn {a, b}, acc -> acc + a * b end)
    else
      0.0
    end
  end

  defp calculate_vector_norm(vector) do
    sum_of_squares = Enum.reduce(vector, 0, fn x, acc -> acc + x * x end)
    :math.sqrt(sum_of_squares)
  end

  defp softmax_normalize(attention_scores) do
    max_score = Enum.max(Enum.map(attention_scores, fn {_node, score} -> score end))
    
    exp_scores = Enum.map(attention_scores, fn {node, score} ->
      {node, :math.exp(score - max_score)}
    end)
    
    total_exp = Enum.reduce(exp_scores, 0, fn {_node, exp_score}, acc -> acc + exp_score end)
    
    Enum.map(exp_scores, fn {node, exp_score} ->
      {node, exp_score / total_exp}
    end)
  end

  defp sample_from_distribution(normalized_scores) do
    random_value = :rand.uniform()
    
    {selected_node, _} = Enum.reduce_while(normalized_scores, {nil, 0.0}, fn {node, prob}, {_acc_node, acc_prob} ->
      new_acc = acc_prob + prob
      if random_value <= new_acc do
        {:halt, {node, new_acc}}
      else
        {:cont, {node, new_acc}}
      end
    end)
    
    selected_node || elem(hd(normalized_scores), 0)
  end

  defp extract_learned_features(mcgs_config) do
    mcgs_config.node_features
  end

  defp calculate_contrastive_loss(mcgs_config) do
    # Simplified contrastive loss calculation
    if length(mcgs_config.contrastive_pairs) > 0 do
      0.5  # Placeholder loss value
    else
      0.0
    end
  end

  defp calculate_representation_quality(avg_positive_sim, avg_negative_sim) do
    # Quality score based on separation between positive and negative similarities
    separation = avg_positive_sim - avg_negative_sim
    max(0.0, min(1.0, separation))
  end
end