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use crate::search::eval;
use crate::search::ordering::MoveQualityEstimator;
use crate::{quiescent, EvalBoard};
use core::cmp;
use myopic_board::{Move, MoveComputeType, Termination};
use std::time::{Duration, Instant};

pub struct SearchContext {
    pub start_time: Instant,
    pub alpha: i32,
    pub beta: i32,
    pub depth_remaining: usize,
    pub precursors: Vec<Move>,
}

impl SearchContext {
    fn next_level(&self, mv: &Move) -> SearchContext {
        let mut next_precursors = self.precursors.clone();
        next_precursors.push(mv.clone());
        SearchContext {
            start_time: self.start_time,
            alpha: -self.beta,
            beta: -self.alpha,
            depth_remaining: self.depth_remaining - 1,
            precursors: next_precursors,
        }
    }
}

/// Represents some object which can determine whether a search should be
/// terminated given certain context about the current state. Implementations
/// are provided for Duration (caps the search based on time elapsed), for
/// usize which represents a maximum search depth and for a pair (Duration, usize)
/// which combines both checks.
pub trait SearchTerminator {
    fn should_terminate(&self, ctx: &SearchContext) -> bool;
}

///
pub struct SearchResponse {
    // The evaluation of the position negamax was called for
    pub eval: i32,
    // The path of optimal play which led to the eval if the
    // depth was greater than zero.
    pub path: Vec<Move>,
}

pub struct Searcher<'a, T, B, M>
where
    T: SearchTerminator,
    B: EvalBoard,
    M: MoveQualityEstimator<B>,
{
    /// The terminator is responsible for deciding when the
    /// search is complete
    pub terminator: &'a T,
    /// The principle variation is a search optimisation which
    /// comes from "iterative deepening". The idea is that if
    /// we do a search at a lower depth then the optimal path
    /// recovered from that is a good candidate to search first
    /// in a deeper search
    pub principle_variation: &'a Vec<Move>,
    /// Used for performing an initial sort on the moves
    /// generated in each position for optimising the search
    pub move_quality_estimator: M,
    /// Placeholder to satisfy the compiler because of the 'unused'
    /// type parameter for the board
    pub board_type: std::marker::PhantomData<B>,
}

impl<T, B, M> Searcher<'_, T, B, M>
where
    T: SearchTerminator,
    B: EvalBoard,
    M: MoveQualityEstimator<B>,
{
    ///
    pub fn search(&self, root: &mut B, mut ctx: SearchContext) -> Result<SearchResponse, String> {
        if self.terminator.should_terminate(&ctx) {
            return Err(format!("Terminated at depth {}", ctx.depth_remaining));
        } else if ctx.depth_remaining == 0 || root.termination_status().is_some() {
            return Ok(SearchResponse {
                eval: match root.termination_status() {
                    Some(Termination::Loss) => eval::LOSS_VALUE,
                    Some(Termination::Draw) => eval::DRAW_VALUE,
                    None => quiescent::search(root, -eval::INFTY, eval::INFTY, -1),
                },
                path: vec![],
            });
        }
        let mut result = -eval::INFTY;
        let mut best_path = vec![];
        for evolve in self.compute_moves(root, &ctx.precursors) {
            let discards = root.evolve(&evolve);
            let SearchResponse { eval, path } = self.search(root, ctx.next_level(&evolve))?;
            root.devolve(&evolve, discards);

            let negated_eval = -eval;
            if negated_eval > result {
                result = negated_eval;
                best_path = path;
                best_path.push(evolve.clone());
            }

            ctx.alpha = cmp::max(ctx.alpha, result);
            if ctx.alpha > ctx.beta {
                return Ok(SearchResponse {
                    eval: ctx.beta,
                    path: vec![],
                });
            }
        }
        return Ok(SearchResponse {
            eval: result,
            path: best_path,
        });
    }

    fn compute_moves(&self, board: &mut B, precursors: &Vec<Move>) -> Vec<Move> {
        let mut moves = board.compute_moves(MoveComputeType::All);
        // Make an initial heuristic sort of the moves before looking
        // for the principle variation
        moves.sort_by_cached_key(|m| -self.move_quality_estimator.estimate(board, m));
        // If we are searching along the principal variation then search the next
        // move on it first (if another move exists)
        if self.principle_variation.starts_with(precursors.as_slice()) {
            match self.principle_variation.get(precursors.len()) {
                None => {}
                Some(suggested_move) => {
                    match moves.iter().position(|m| m == suggested_move) {
                        None => {} // Some sort of debug warning?
                        Some(index) => {
                            moves.remove(index);
                            moves.insert(0, suggested_move.clone());
                        }
                    }
                }
            }
        }
        moves
    }
}

impl SearchTerminator for Duration {
    fn should_terminate(&self, ctx: &SearchContext) -> bool {
        ctx.start_time.elapsed() > *self
    }
}

impl SearchTerminator for usize {
    fn should_terminate(&self, ctx: &SearchContext) -> bool {
        ctx.depth_remaining > *self
    }
}

impl SearchTerminator for (Duration, usize) {
    fn should_terminate(&self, ctx: &SearchContext) -> bool {
        self.0.should_terminate(ctx) || self.1.should_terminate(ctx)
    }
}