// Copyright 2020 Xavier Gillard
//
// Permission is hereby granted, free of charge, to any person obtaining a copy of
// this software and associated documentation files (the "Software"), to deal in
// the Software without restriction, including without limitation the rights to
// use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
// the Software, and to permit persons to whom the Software is furnished to do so,
// subject to the following conditions:
//
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//
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// FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
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//! This example show how to implement a solver for the pigment sequencing problem 
//! using ddo. It is a fairly simple example but it features most of the aspects you will
//! want to copy when implementing your own solver.

use std::{time::{Duration, Instant}};

use clap::Parser;
use ddo::*;

use crate::{io_utils::read_instance, model::{PspRelax, PspRanking}};

mod model;
mod io_utils;
mod ub_utils;

#[cfg(test)]
mod tests;


// #########################################################################################
// # THE INFORMATION BEYOND THIS LINE IS NOT DIRECTLY RELATED TO THE IMPLEMENTATION OF     #
// # A SOLVER BASED ON DDO. INSTEAD, THAT PORTION OF THE CODE CONTAINS GENERIC FUNCTION    #
// # THAT ARE USED TO READ AN INSTANCE FROM FILE, PROCESS COMMAND LINE ARGUMENTS, AND      #
// # THE MAIN FUNCTION. THESE ARE THUS NOT REQUIRED 'PER-SE', BUT I BELIEVE IT IS USEFUL   #
// # TO SHOW HOW IT CAN BE DONE IN AN EXAMPLE.                                             #
// #########################################################################################

/// This structure uses `clap-derive` annotations and define the arguments that can
/// be passed on to the executable solver.
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
    /// The path to the instance file
    fname: String,
    /// The number of concurrent threads
    #[clap(short, long, default_value = "8")]
    threads: usize,
    /// The maximum amount of time you would like this solver to run
    #[clap(short, long)]
    duration: Option<u64>,
    /// The maximum number of nodes per layer
    #[clap(short, long)]
    width: Option<usize>,
}

/// An utility function to return an max width heuristic that can either be a fixed width
/// policy (if w is fixed) or an adaptive policy returning the number of unassigned variables
/// in the overall problem.
fn max_width<P: Problem>(p: &P, w: Option<usize>) -> Box<dyn WidthHeuristic<P::State> + Send + Sync> {
    if let Some(w) = w {
        Box::new(FixedWidth(w))
    } else {
        Box::new(NbUnassignedWidth(p.nb_variables()))
    }
}
/// An utility function to return a cutoff heuristic that can either be a time budget policy
/// (if timeout is fixed) or no cutoff policy.
fn cutoff(timeout: Option<u64>) -> Box<dyn Cutoff + Send + Sync> {
    if let Some(t) = timeout {
        Box::new(TimeBudget::new(Duration::from_secs(t)))
    } else {
        Box::new(NoCutoff)
    }
}

/// This is your executable's entry point. It is the place where all the pieces are put together
/// to create a fast an effective solver for the knapsack problem.
fn main() {
    let args = Args::parse();
    let fname = &args.fname;
    let problem = read_instance(fname).unwrap();
    let relaxation = PspRelax::new(&problem);
    let ranking = PspRanking;

    let width = max_width(&problem, args.width);
    let dominance = EmptyDominanceChecker::default();
    let cutoff = cutoff(args.duration);
    let mut fringe = NoDupFringe::new(MaxUB::new(&ranking));

    // This solver compile DD that allow the definition of long arcs spanning over several layers.
    let mut solver = DefaultCachingSolver::custom(
        &problem, 
        &relaxation, 
        &ranking, 
        width.as_ref(), 
        &dominance,
        cutoff.as_ref(), 
        &mut fringe,
        args.threads,
    );

    let start = Instant::now();
    let Completion{ is_exact, best_value } = solver.maximize();
    
    let duration = start.elapsed();
    let upper_bound = solver.best_upper_bound();
    let lower_bound = solver.best_lower_bound();
    let gap = solver.gap();
    let best_solution: Option<Vec<_>>  = solver.best_solution()
        .map(|mut decisions|{
            decisions.sort_unstable_by_key(|d| d.variable.id());
            decisions.iter()
                .map(|d| d.value)
                .collect()
        });
    
    println!("Duration:   {:.3} seconds", duration.as_secs_f32());
    println!("Objective:  {}",            best_value.map(|v| -v).unwrap_or(-1));
    println!("Upper Bnd:  {}",            -upper_bound);
    println!("Lower Bnd:  {}",            -lower_bound);
    println!("Gap:        {:.3}",         gap);
    println!("Aborted:    {}",            !is_exact);
    println!("Solution:   {:?}",          best_solution.unwrap_or_default());
}