defmodule Yog.Pathfinding.AStar do
  @moduledoc """
  A* (A-Star) search algorithm for optimal pathfinding with heuristic guidance.

  A* combines Dijkstra's algorithm with a heuristic function to efficiently find
  the shortest path in weighted graphs. It prioritizes exploration toward the goal
  using the evaluation function: f(n) = g(n) + h(n).

  ## Algorithm Characteristics

  - **Time Complexity**: O((V + E) log V) with a good heuristic
  - **Space Complexity**: O(V)
  - **Requirements**: Non-negative edge weights, admissible heuristic
  - **Optimality**: Optimal if heuristic is admissible (never overestimates)

  ## The Heuristic Function

  The heuristic `h(n)` estimates the cost from node `n` to the goal. For A* to be
  optimal, the heuristic must be:
  - **Admissible**: Never overestimates the true cost
  - **Consistent**: h(n) ≤ c(n,n') + h(n') for all edges

  ## When to Use

  - When you have a good heuristic (e.g., Euclidean distance on grids)
  - Pathfinding in games and robotics
  - When the goal is known and you want faster search than Dijkstra

  ## Examples

      # Grid pathfinding with Manhattan distance heuristic
      heuristic = fn {x1, y1}, {x2, y2} -> abs(x1-x2) + abs(y1-y2) end
      Yog.Pathfinding.AStar.a_star(graph, start, goal, 0, &(&1+&2), &Integer.compare/2, heuristic)
  """

  alias Yog.Model
  alias Yog.Pathfinding.Path
  alias Yog.PriorityQueue, as: PQ

  @typedoc "Result type for shortest path queries"
  @type path_result :: {:ok, Path.t()} | :error

  # ============================================================
  # Keyword-style API (for Pathfinding module delegation)
  # ============================================================

  @doc """
  Find shortest path using A* with keyword options.

  ## Options

    * `:in` - The graph to search
    * `:from` - Starting node
    * `:to` - Target node
    * `:zero` - Identity value for the weight type
    * `:add` - Function to add two weights
    * `:compare` - Function to compare weights (`:lt`, `:eq`, `:gt`)
    * `:heuristic` - Function estimating cost to goal: `fn(node) -> cost`

  ## Examples

      Yog.Pathfinding.AStar.a_star(
        in: graph,
        from: :a,
        to: :c,
        zero: 0,
        add: &(&1 + &2),
        compare: &Integer.compare/2,
        heuristic: fn node -> estimate_to_goal(node) end
      )
  """
  @spec a_star(keyword()) :: path_result()
  def a_star(opts) do
    graph = Keyword.fetch!(opts, :in)
    from = Keyword.fetch!(opts, :from)
    to = Keyword.fetch!(opts, :to)
    zero = opts[:zero] || 0
    add = opts[:add] || (&Kernel.+/2)
    compare = opts[:compare] || (&Yog.Utils.compare/2)
    heuristic = Keyword.fetch!(opts, :heuristic)

    a_star(graph, from, to, heuristic, zero, add, compare)
  end

  @doc """
  Implicit A* using keyword options.

  ## Options

    * `:from` - Starting state
    * `:successors_with_cost` - Function returning neighbors with costs
    * `:is_goal` - Function to check if a state is the goal
    * `:zero` - Identity value for the weight type
    * `:add` - Function to add two weights
    * `:compare` - Function to compare weights
    * `:heuristic` - Function estimating cost to goal: `fn(state) -> cost`

  ## Examples

      Yog.Pathfinding.AStar.implicit_a_star(
        from: 1,
        successors_with_cost: fn n -> [{n+1, 1}] end,
        is_goal: fn n -> n == 10 end,
        zero: 0,
        add: &(&1 + &2),
        compare: &Integer.compare/2,
        heuristic: fn n -> 10 - n end
      )
  """
  @spec implicit_a_star(keyword()) :: {:ok, any()} | :error
  def implicit_a_star(opts) do
    from = Keyword.fetch!(opts, :from)
    successors = Keyword.fetch!(opts, :successors_with_cost)
    is_goal = Keyword.fetch!(opts, :is_goal)
    zero = opts[:zero] || 0
    add = opts[:add] || (&Kernel.+/2)
    compare = opts[:compare] || (&Yog.Utils.compare/2)
    heuristic = Keyword.fetch!(opts, :heuristic)

    implicit_a_star(from, successors, is_goal, heuristic, zero, add, compare)
  end

  @doc """
  Implicit A* with key function using keyword options.

  ## Options

    * `:from` - Starting state
    * `:successors_with_cost` - Function returning neighbors with costs
    * `:visited_by` - Function to extract a key for visited tracking
    * `:is_goal` - Function to check if a state is the goal
    * `:zero` - Identity value for the weight type
    * `:add` - Function to add two weights
    * `:compare` - Function to compare weights
    * `:heuristic` - Function estimating cost to goal: `fn(state) -> cost`
  """
  @spec implicit_a_star_by(keyword()) :: {:ok, any()} | :error
  def implicit_a_star_by(opts) do
    from = Keyword.fetch!(opts, :from)
    successors = Keyword.fetch!(opts, :successors_with_cost)
    visited_by = Keyword.fetch!(opts, :visited_by)
    is_goal = Keyword.fetch!(opts, :is_goal)
    zero = opts[:zero] || 0
    add = opts[:add] || (&Kernel.+/2)
    compare = opts[:compare] || (&Yog.Utils.compare/2)
    heuristic = Keyword.fetch!(opts, :heuristic)

    implicit_a_star_by(from, successors, visited_by, is_goal, heuristic, zero, add, compare)
  end

  # ============================================================
  # Direct API
  # ============================================================

  @doc """
  Find the shortest path using A* with a heuristic function.

  ## Parameters

    * `graph` - The graph to search
    * `from` - Starting node
    * `to` - Target node
    * `zero` - Identity value for the weight type
    * `add` - Function to add two weights
    * `compare` - Function to compare weights (`:lt`, `:eq`, `:gt`)
    * `heuristic` - Function `fn(node, goal) -> cost` estimating cost to goal

  ## Returns

    * `{:ok, path}` - A `Path` struct containing the nodes and total weight
    * `:error` - No path exists between the nodes

  ## Examples

      iex> graph = Yog.directed()
      ...> |> Yog.add_node(:a, nil)
      ...> |> Yog.add_node(:b, nil)
      ...> |> Yog.add_node(:c, nil)
      ...> |> Yog.add_edge!(:a, :b, 4)
      ...> |> Yog.add_edge!(:b, :c, 1)
      iex> # Admissible heuristic (never overestimates)
      iex> h = fn _, _ -> 0 end  # Zero heuristic = Dijkstra
      iex> compare = &Yog.Utils.compare/2
      iex> {:ok, path} = Yog.Pathfinding.AStar.a_star(graph, :a, :c, h, 0, &(&1 + &2), compare)
      iex> path.nodes
      [:a, :b, :c]
      iex> path.weight
      5

      iex> # Grid with Manhattan distance heuristic
      iex> grid = Yog.directed()
      ...> |> Yog.add_edge!({0,0}, {1,0}, 1)
      ...> |> Yog.add_edge!({1,0}, {2,0}, 1)
      iex> manhattan = fn {x1, y1}, {x2, y2} -> abs(x1-x2) + abs(y1-y2) end
      iex> compare = &Yog.Utils.compare/2
      iex> {:ok, path} = Yog.Pathfinding.AStar.a_star(grid, {0,0}, {2,0}, manhattan, 0, &(&1+&2), compare)
      iex> path.nodes
      [{0,0}, {1,0}, {2,0}]
      iex> path.weight
      2
  """
  @spec a_star(
          Yog.graph(),
          Yog.node_id(),
          Yog.node_id(),
          (Yog.node_id(), Yog.node_id() -> weight),
          weight,
          (weight, weight -> weight),
          (weight, weight -> :lt | :eq | :gt)
        ) :: path_result()
        when weight: var
  def a_star(
        graph,
        from,
        to,
        heuristic,
        zero \\ 0,
        add \\ &Kernel.+/2,
        compare \\ &Yog.Utils.compare/2
      ) do
    # Priority queue: {f_score, g_score, node, path}
    # f(n) = g(n) + h(n)
    h0 = heuristic.(from, to)

    initial_queue =
      PQ.new(fn {f1, _, _, _}, {f2, _, _, _} -> compare.(f1, f2) != :gt end)
      |> PQ.push({add.(zero, h0), zero, from, [from]})

    initial_g_scores = %{from => zero}

    do_a_star(graph, initial_queue, to, add, compare, heuristic, initial_g_scores)
  end

  @doc """
  Run A* on an implicit (generated) graph.

  Similar to implicit Dijkstra, but uses a heuristic to guide search toward the goal.

  ## Parameters

    * `from` - Starting state
    * `successors` - Function `state -> [{neighbor, cost}]`
    * `is_goal` - Function `state -> boolean` to check if goal reached
    * `zero` - Identity value for the weight type
    * `add` - Function to add two weights
    * `compare` - Function to compare weights
    * `heuristic` - Function `fn(state) -> cost` estimating cost to goal

  ## Returns

    * `{:ok, cost}` - Minimum cost to reach goal
    * `:error` - Goal is unreachable

  ## Examples

      # Search with heuristic guidance
      iex> successors = fn
      ...>   1 -> [{2, 1}]
      ...>   2 -> [{3, 2}]
      ...>   3 -> [{4, 3}]
      ...>   4 -> []
      ...> end
      iex> # Heuristic: distance to goal (node 4)
      iex> h = fn n -> 4 - n end
      iex> compare = &Yog.Utils.compare/2
      iex> {:ok, cost} = Yog.Pathfinding.AStar.implicit_a_star(
      ...>   1, successors, fn x -> x == 4 end, h,
      ...>   0, &(&1 + &2), compare
      ...> )
      iex> cost
      6
  """
  @spec implicit_a_star(
          state,
          (state -> [{state, cost}]),
          (state -> boolean),
          (state -> cost),
          cost,
          (cost, cost -> cost),
          (cost, cost -> :lt | :eq | :gt)
        ) :: {:ok, cost} | :error
        when state: var, cost: var
  def implicit_a_star(
        from,
        successors,
        is_goal,
        heuristic,
        zero \\ 0,
        add \\ &Kernel.+/2,
        compare \\ &Yog.Utils.compare/2
      ) do
    implicit_a_star_by(from, successors, fn x -> x end, is_goal, heuristic, zero, add, compare)
  end

  @doc """
  Implicit A* with a key function for visited state tracking.

  Similar to `implicit_a_star/7`, but uses a key function for visited tracking.

  ## Parameters

    * `from` - Starting state
    * `successors` - Function `state -> [{neighbor, cost}]`
    * `key_fn` - Function `state -> key` for visited tracking
    * `is_goal` - Function `state -> boolean` to check if goal reached
    * `zero` - Identity value for the weight type
    * `add` - Function to add two weights
    * `compare` - Function to compare weights
    * `heuristic` - Function `fn(state) -> cost` estimating cost to goal

  ## Examples

      iex> _successors = fn {x, y, _dir} -> [{{x + 1, y, :east}, 1}, {{x, y + 1, :south}, 1}] end
      iex> _key_fn = fn {x, y, _dir} -> {x, y} end
      iex> _h = fn {x, y, _} -> (10 - x) + (10 - y) end
      iex> _goal_fn = fn {x, y, _} -> x == 10 and y == 10 end
      iex> _compare = &Yog.Utils.compare/2
      iex> #Yog.Pathfinding.AStar.implicit_a_star_by(
      ...> #  {0, 0, :north}, _successors, _key_fn,
      ...> #  _goal_fn, 0, &(&1 + &2), _compare, _h
      ...> #)
      ...> # {:some, 20}
  """
  @spec implicit_a_star_by(
          state,
          (state -> [{state, cost}]),
          (state -> term()),
          (state -> boolean),
          (state -> cost),
          cost,
          (cost, cost -> cost),
          (cost, cost -> :lt | :eq | :gt)
        ) :: {:ok, cost} | :error
        when state: var, cost: var
  def implicit_a_star_by(
        from,
        successors,
        key_fn,
        is_goal,
        heuristic,
        zero \\ 0,
        add \\ &Kernel.+/2,
        compare \\ &Yog.Utils.compare/2
      ) do
    h0 = heuristic.(from)
    # Queue: {f_score, g_score, state}
    initial_queue =
      PQ.new(fn {f1, _, _}, {f2, _, _} -> compare.(f1, f2) != :gt end)
      |> PQ.push({add.(zero, h0), zero, from})

    initial_g_scores = %{key_fn.(from) => zero}

    do_implicit_a_star(
      initial_queue,
      successors,
      key_fn,
      is_goal,
      add,
      compare,
      heuristic,
      initial_g_scores
    )
  end

  # Main A* implementation for materialized graphs
  defp do_a_star(graph, queue, to, add, compare, heuristic, g_scores) do
    case PQ.pop(queue) do
      :error ->
        :error

      {:ok, {_f, g, node, path}, rest} ->
        key = node

        # Check if this entry is outdated
        case Map.fetch(g_scores, key) do
          {:ok, best_g} ->
            if compare.(g, best_g) == :gt do
              do_a_star(graph, rest, to, add, compare, heuristic, g_scores)
            else
              if node == to do
                {:ok, Path.new(Enum.reverse(path), g, :a_star)}
              else
                # Expand neighbors
                successors = Model.successors(graph, node)

                {new_queue, new_g_scores} =
                  Enum.reduce(successors, {rest, g_scores}, fn {neighbor, cost}, {q, gs} ->
                    new_g = add.(g, cost)
                    neighbor_key = neighbor

                    case Map.fetch(gs, neighbor_key) do
                      {:ok, existing_g} ->
                        if compare.(new_g, existing_g) == :lt do
                          h = heuristic.(neighbor, to)
                          f = add.(new_g, h)
                          new_q = PQ.push(q, {f, new_g, neighbor, [neighbor | path]})
                          new_gs = Map.put(gs, neighbor_key, new_g)
                          {new_q, new_gs}
                        else
                          {q, gs}
                        end

                      :error ->
                        h = heuristic.(neighbor, to)
                        f = add.(new_g, h)
                        new_q = PQ.push(q, {f, new_g, neighbor, [neighbor | path]})
                        new_gs = Map.put(gs, neighbor_key, new_g)
                        {new_q, new_gs}
                    end
                  end)

                do_a_star(graph, new_queue, to, add, compare, heuristic, new_g_scores)
              end
            end

          :error ->
            do_a_star(graph, rest, to, add, compare, heuristic, g_scores)
        end
    end
  end

  # Implicit A* implementation
  defp do_implicit_a_star(
         queue,
         successors,
         key_fn,
         is_goal,
         add,
         compare,
         heuristic,
         g_scores
       ) do
    case PQ.pop(queue) do
      :error ->
        :error

      {:ok, {_f, g, state}, rest} ->
        key = key_fn.(state)

        # Check if this entry is outdated
        case Map.fetch(g_scores, key) do
          {:ok, best_g} ->
            if compare.(g, best_g) == :gt do
              do_implicit_a_star(
                rest,
                successors,
                key_fn,
                is_goal,
                add,
                compare,
                heuristic,
                g_scores
              )
            else
              if is_goal.(state) do
                {:ok, g}
              else
                # Expand successors
                next_states = successors.(state)

                {new_queue, new_g_scores} =
                  Enum.reduce(next_states, {rest, g_scores}, fn {next_state, cost}, {q, gs} ->
                    new_g = add.(g, cost)
                    next_key = key_fn.(next_state)

                    case Map.fetch(gs, next_key) do
                      {:ok, existing_g} ->
                        if compare.(new_g, existing_g) == :lt do
                          h = heuristic.(next_state)
                          f = add.(new_g, h)
                          new_q = PQ.push(q, {f, new_g, next_state})
                          new_gs = Map.put(gs, next_key, new_g)
                          {new_q, new_gs}
                        else
                          {q, gs}
                        end

                      :error ->
                        h = heuristic.(next_state)
                        f = add.(new_g, h)
                        new_q = PQ.push(q, {f, new_g, next_state})
                        new_gs = Map.put(gs, next_key, new_g)
                        {new_q, new_gs}
                    end
                  end)

                do_implicit_a_star(
                  new_queue,
                  successors,
                  key_fn,
                  is_goal,
                  add,
                  compare,
                  heuristic,
                  new_g_scores
                )
              end
            end

          :error ->
            do_implicit_a_star(
              rest,
              successors,
              key_fn,
              is_goal,
              add,
              compare,
              heuristic,
              g_scores
            )
        end
    end
  end
end