/*++
  Copyright (c) 2017 Microsoft Corporation

  Module Name:

  sat_local_search.h

  Abstract:
   
  Local search module for cardinality clauses.

  Author:

  Sixue Liu 2017-2-21

  Notes:

  --*/
#pragma once

#include "util/vector.h"
#include "sat/sat_types.h"
#include "sat/sat_config.h"
#include "util/rlimit.h"
#include "util/ema.h"
#include "util/statistics.h"

namespace sat {

    class parallel;

    class local_search_config {
        unsigned          m_random_seed;
        int               m_best_known_value;
        local_search_mode m_mode;
        bool              m_phase_sticky;
        bool              m_dbg_flips;
        double            m_itau;

        friend class local_search;

        void set_config(config const& cfg) {
            m_mode         = cfg.m_local_search_mode;
            m_random_seed  = cfg.m_random_seed;
            m_phase_sticky = cfg.m_phase_sticky;
            m_dbg_flips    = cfg.m_local_search_dbg_flips;
        }

    public:
        local_search_config() {
            m_random_seed = 0;
            m_best_known_value = INT_MAX;
            m_mode = local_search_mode::wsat;
            m_phase_sticky = false;
            m_dbg_flips = false;
            m_itau = 0.5;
        }

        unsigned random_seed() const { return m_random_seed; }
        unsigned best_known_value() const { return m_best_known_value;  }
        local_search_mode mode() const { return m_mode; }
        bool phase_sticky() const { return m_phase_sticky; }
        bool dbg_flips() const { return m_dbg_flips; }
        double itau() const { return m_itau; }
        
        void set_random_seed(unsigned s) { m_random_seed = s;  }
        void set_best_known_value(unsigned v) { m_best_known_value = v; }

    };


    class local_search : public i_local_search {
		
        struct pbcoeff {
            unsigned m_constraint_id;
            unsigned m_coeff;
            pbcoeff(unsigned id, unsigned coeff):
                m_constraint_id(id), m_coeff(coeff) {}
        };
        typedef svector<pbcoeff> coeff_vector;


        struct stats {
            unsigned m_num_flips;
            unsigned m_num_restarts;
            void reset() { memset(this, 0, sizeof(*this)); }
            stats() { reset(); }
        };
        
        struct var_info {
            bool m_value{ true };                        // current solution
            unsigned m_bias{ 50 };                     // bias for current solution in percentage.
                                                 // if bias is 0, then value is always false, if 100, then always true
            bool m_unit{ false };                         // is this a unit literal
            literal m_explain;                   // explanation for unit assignment
            bool m_conf_change{ true };                  // whether its configure changes since its last flip
            bool m_in_goodvar_stack{ false };
            int  m_score{ 0 };
            int  m_slack_score{ 0 };
            int  m_time_stamp{ 0 };                   // the flip time stamp                 
            bool_var_vector m_neighbors;         // neighborhood variables
            coeff_vector m_watch[2];
            literal_vector m_bin[2];
            unsigned m_flips{ 0 };
            ema  m_slow_break;
            double m_break_prob{ 0 };
            var_info():
                m_slow_break(1e-5)
            {}
        };

        struct constraint {
            unsigned        m_id;
            unsigned        m_k;
            int64_t         m_slack;
            unsigned        m_size;
            literal_vector  m_literals;
            constraint(unsigned k, unsigned id) : m_id(id), m_k(k), m_slack(0), m_size(0) {}
            void push(literal l) { m_literals.push_back(l); ++m_size; }
            unsigned size() const { return m_size; }
            literal const& operator[](unsigned idx) const { return m_literals[idx]; }
            literal const* begin() const { return m_literals.begin(); }
            literal const* end() const { return m_literals.end(); }
        };


        stats               m_stats;
        local_search_config m_config;        
        
        vector<var_info>    m_vars;                      // variables
        bool_vector       m_best_phase;                // best value in round
        svector<bool_var>   m_units;                     // unit clauses
        vector<constraint>  m_constraints;               // all constraints
        literal_vector      m_assumptions;               // temporary assumptions
        literal_vector      m_prop_queue;                // propagation queue
        unsigned            m_num_non_binary_clauses = 0;
        bool                m_is_pb = false;
        bool                m_is_unsat = false;
        unsigned_vector     m_unsat_stack;               // store all the unsat constraints
        unsigned_vector     m_index_in_unsat_stack;      // which position is a constraint in the unsat_stack
        
        // configuration changed decreasing variables (score>0 and conf_change==true)
        bool_var_vector  m_goodvar_stack;
        bool             m_initializing = false;


        // information about solution
        unsigned         m_best_unsat = 0;
        double           m_best_unsat_rate = 0;
        double           m_last_best_unsat_rate = 0;
                                                       // for non-known instance, set as maximal
        int              m_best_known_value = INT_MAX; // best known value for this instance
        
        unsigned         m_max_steps = (1 << 30);
        
        // dynamic noise
        double m_noise = 9800;                         // normalized by 10000
        double m_noise_delta = 0.05;

        reslimit    m_limit;
        random_gen  m_rand;
        parallel* m_par = nullptr;
        model       m_model;

        inline int score(bool_var v) const { return m_vars[v].m_score; }
        inline void inc_score(bool_var v) { m_vars[v].m_score++; }
        inline void dec_score(bool_var v) { m_vars[v].m_score--; }

        inline int slack_score(bool_var v) const { return m_vars[v].m_slack_score; }
        inline void inc_slack_score(bool_var v) { m_vars[v].m_slack_score++; }
        inline void dec_slack_score(bool_var v) { m_vars[v].m_slack_score--; }
        
        inline bool already_in_goodvar_stack(bool_var v) const { return m_vars[v].m_in_goodvar_stack; }
        inline bool conf_change(bool_var v) const { return m_vars[v].m_conf_change; }
        inline int  time_stamp(bool_var v) const { return m_vars[v].m_time_stamp; }
        
        inline void set_best_unsat();
        /* TBD: other scores */


        inline bool is_pos(literal t) const { return !t.sign(); }
        inline bool is_true(bool_var v) const { return cur_solution(v); }
        inline bool is_true(literal l) const { return cur_solution(l.var()) != l.sign(); }
        inline bool is_false(literal l) const { return cur_solution(l.var()) == l.sign(); }
        inline bool is_unit(bool_var v) const { return m_vars[v].m_unit; }
        inline bool is_unit(literal l) const { return m_vars[l.var()].m_unit; }

        unsigned num_constraints() const { return m_constraints.size(); } // constraint index from 1 to num_constraint

        int64_t constraint_slack(unsigned ci) const { return m_constraints[ci].m_slack; }

        void init();
        void reinit();
        void reinit_orig();
        void init_cur_solution();
        void init_slack();
        void init_scores();
        void init_goodvars();        
        void pick_flip_lookahead();
        void pick_flip_walksat();
        void flip_walksat(bool_var v);
        bool propagate(literal lit);
        void add_propagation(literal lit);
        void walksat();
        void unsat(unsigned c);
        void sat(unsigned c);
        void set_parameters();
        void verify_solution() const;
        void verify_unsat_stack() const;
        void verify_constraint(constraint const& c) const;
        void verify_slack(constraint const& c) const;
        void verify_slack() const;
        bool verify_goodvar() const;
        uint64_t constraint_value(constraint const& c) const;
        unsigned constraint_coeff(constraint const& c, literal l) const;
        void print_info(std::ostream& out);
        void extract_model();
        void add_clause(unsigned sz, literal const* c);
        void add_unit(literal lit, literal explain);

        std::ostream& display(std::ostream& out) const;
        std::ostream& display(std::ostream& out, constraint const& c) const;
        std::ostream& display(std::ostream& out, unsigned v, var_info const& vi) const;

        lbool check();

        unsigned num_vars() const { return m_vars.size() - 1; }     // var index from 1 to num_vars

    public:
        
        reslimit& rlimit() override { return m_limit; }

        lbool check(unsigned sz, literal const* assumptions, parallel* p) override;

        unsigned num_non_binary_clauses() const override { return m_num_non_binary_clauses; }

        void add(solver const& s) override { import(s, false); }

        model const& get_model() const override { return m_model; }

        void collect_statistics(statistics& st) const override;        

        void updt_params(params_ref const& p) override {}

        void set_seed(unsigned n) override { config().set_random_seed(n); }

        void reinit(solver& s, bool_vector const& phase) override;

        // used by unit-walk
        void set_phase(bool_var v, bool f);

        void set_bias(bool_var v, lbool f);

        bool get_best_phase(bool_var v) const { return m_best_phase[v]; }

        inline bool cur_solution(bool_var v) const { return m_vars[v].m_value; }

        double get_priority(bool_var v) const override { return m_vars[v].m_break_prob; }

        void import(solver const& s, bool init);        

        void add_cardinality(unsigned sz, literal const* c, unsigned k);

        void add_pb(unsigned sz, literal const* c, unsigned const* coeffs, unsigned k);

        local_search_config& config() { return m_config;  }

    };
}