"""Greedy Best-First Search algorithm."""

from typing import Tuple, Optional, List, Callable, TYPE_CHECKING

if TYPE_CHECKING:
    from ..core.generic_search import GenericSearch

from ..core.node import SearchNode
from ..core.frontier import PriorityQueueFrontier
from ..models.state import PathResult, SearchStep


def greedy_search(
    problem: "GenericSearch",
    heuristic: Callable[[Tuple[int, int], Tuple[int, int]], float],
    visualize: bool = False,
) -> Tuple[PathResult, Optional[List[SearchStep]]]:
    """
    Greedy best-first search using heuristic only.

    Expands node that appears closest to goal.
    Not guaranteed to find optimal solution.
    Not complete in general.

    Args:
        problem: The search problem to solve
        heuristic: Function(state, goal) -> estimated cost to goal
        visualize: If True, collect visualization steps

    Returns:
        Tuple of (PathResult, Optional[List[SearchStep]])
    """
    frontier = PriorityQueueFrontier()
    start = problem.initial_state()

    # Get goal for heuristic calculation (assume single goal)
    goal = getattr(problem, "goal", None)

    h_value = heuristic(start, goal) if goal else 0
    start_node = SearchNode(state=start, path_cost=0, depth=0, priority=h_value)
    frontier.push(start_node)

    explored: set = set()
    nodes_expanded = 0
    steps: List[SearchStep] = [] if visualize else None

    while not frontier.is_empty():
        node = frontier.pop()

        # Record step for visualization
        if visualize:
            steps.append(
                SearchStep(
                    step_number=nodes_expanded,
                    current_node=node.state,
                    action=node.action,
                    frontier=frontier.get_states(),
                    explored=list(explored),
                    current_path=node.get_path(),
                    path_cost=node.path_cost,
                )
            )

        # Goal test
        if problem.goal_test(node.state):
            return (
                PathResult(
                    plan=node.get_solution(),
                    cost=node.path_cost,
                    nodes_expanded=nodes_expanded,
                    path=node.get_path(),
                ),
                steps,
            )

        # Skip if already explored
        if node.state in explored:
            continue

        explored.add(node.state)
        nodes_expanded += 1

        # Expand node
        for action in problem.actions(node.state):
            child_state = problem.result(node.state, action)

            if child_state not in explored:
                step_cost = problem.step_cost(node.state, action, child_state)
                h_value = heuristic(child_state, goal) if goal else 0

                child = SearchNode(
                    state=child_state,
                    parent=node,
                    action=action,
                    path_cost=node.path_cost + step_cost,
                    depth=node.depth + 1,
                    priority=h_value,  # Priority = h(n) only for Greedy
                )
                frontier.push(child)

    # No solution found
    return (
        PathResult(plan="", cost=float("inf"), nodes_expanded=nodes_expanded, path=[]),
        steps,
    )