#
# This file is part of pySMT.
#
#   Copyright 2014 Andrea Micheli and Marco Gario
#
#   Licensed under the Apache License, Version 2.0 (the "License");
#   you may not use this file except in compliance with the License.
#   You may obtain a copy of the License at
#
#       http://www.apache.org/licenses/LICENSE-2.0
#
#   Unless required by applicable law or agreed to in writing, software
#   distributed under the License is distributed on an "AS IS" BASIS,
#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#   See the License for the specific language governing permissions and
#   limitations under the License.
#
"""Factories are used to build new Solvers or Quantifier Eliminators
without the need of specifying them. For example, the user can simply
require a Solver that is able to deal with quantified theories, and
the factory will return one such solver among the available ones.
This makes it possible to write algorithms that do not depend on a
particular solver.
"""

from functools import partial

from pysmt.exceptions import (NoSolverAvailableError, SolverRedefinitionError,
                              NoLogicAvailableError,
                              SolverAPINotFound)
from pysmt.logics import QF_UFLIRA, LRA, QF_UFLRA
from pysmt.logics import AUTO as AUTO_LOGIC
from pysmt.logics import most_generic_logic, get_closer_logic
from pysmt.logics import convert_logic_from_string
from pysmt.oracles import get_logic
from pysmt.solvers.qelim import (ShannonQuantifierEliminator,
                                 SelfSubstitutionQuantifierEliminator)
from pysmt.solvers.solver import SolverOptions
from pysmt.solvers.portfolio import Portfolio

DEFAULT_SOLVER_PREFERENCE_LIST = ['msat', 'z3', 'cvc4', 'yices', 'btor',
                                  'picosat', 'bdd']
DEFAULT_QELIM_PREFERENCE_LIST = ['z3', 'msat_fm', 'msat_lw', 'bdd',
                                 'shannon', 'selfsub']
DEFAULT_INTERPOLATION_PREFERENCE_LIST = ['msat']
DEFAULT_LOGIC = QF_UFLIRA
DEFAULT_QE_LOGIC = LRA
DEFAULT_INTERPOLATION_LOGIC = QF_UFLRA


class Factory(object):
    """Factory used to build Solver, QuantifierEliminators, Interpolators etc.

    This class contains the logic to magically select the correct
    solver. Moreover, this is the class providing the shortcuts
    is_sat, is_unsat etc.

    """
    def __init__(self, environment,
                 solver_preference_list=None,
                 qelim_preference_list=None,
                 interpolation_preference_list=None):
        self.environment = environment
        self._all_solvers = None
        self._all_unsat_core_solvers = None
        self._all_qelims = None
        self._all_interpolators = None
        self._generic_solvers = {}

        #
        if solver_preference_list is None:
            solver_preference_list = DEFAULT_SOLVER_PREFERENCE_LIST
        self.solver_preference_list = solver_preference_list
        if qelim_preference_list is None:
            qelim_preference_list = DEFAULT_QELIM_PREFERENCE_LIST
        self.qelim_preference_list = qelim_preference_list
        if interpolation_preference_list is None:
            interpolation_preference_list = DEFAULT_INTERPOLATION_PREFERENCE_LIST
        self.interpolation_preference_list = interpolation_preference_list
        #
        self._default_logic = DEFAULT_LOGIC
        self._default_qe_logic = DEFAULT_QE_LOGIC
        self._default_interpolation_logic = DEFAULT_INTERPOLATION_LOGIC
        self._get_available_solvers()
        self._get_available_qe()
        self._get_available_interpolators()


    def get_solver(self, name=None, logic=None, **options):
        SolverClass, closer_logic = \
           self._get_solver_class(solver_list=self._all_solvers,
                                  solver_type="Solver",
                                  preference_list=self.solver_preference_list,
                                  default_logic=self.default_logic,
                                  name=name,
                                  logic=logic)

        return SolverClass(environment=self.environment,
                           logic=closer_logic,
                           **options)


    def get_unsat_core_solver(self, name=None, logic=None,
                              unsat_cores_mode="all", **options):
        SolverClass, closer_logic = \
           self._get_solver_class(solver_list=self._all_unsat_core_solvers,
                                  solver_type="Solver supporting Unsat Cores",
                                  preference_list=self.solver_preference_list,
                                  default_logic=self.default_logic,
                                  name=name,
                                  logic=logic)
        return SolverClass(environment=self.environment,
                           logic=closer_logic,
                           generate_models=True,
                           unsat_cores_mode=unsat_cores_mode,
                           **options)

    def get_quantifier_eliminator(self, name=None, logic=None):
        SolverClass, closer_logic = \
           self._get_solver_class(solver_list=self._all_qelims,
                                  solver_type="Quantifier Eliminator",
                                  preference_list=self.qelim_preference_list,
                                  default_logic=self.default_qe_logic,
                                  name=name,
                                  logic=logic)

        return SolverClass(environment=self.environment,
                           logic=closer_logic)

    def get_interpolator(self, name=None, logic=None):
        SolverClass, closer_logic = \
           self._get_solver_class(solver_list=self._all_interpolators,
                                  solver_type="Interpolator",
                                  preference_list=self.interpolation_preference_list,
                                  default_logic=self._default_interpolation_logic,
                                  name=name,
                                  logic=logic)

        return SolverClass(environment=self.environment,
                           logic=closer_logic)


    def _get_solver_class(self, solver_list, solver_type, preference_list,
                          default_logic, name=None, logic=None):
        if len(solver_list) == 0:
            raise NoSolverAvailableError("No %s is available" % solver_type)

        logic = convert_logic_from_string(logic)
        if name is not None:
            if name not in solver_list:
                raise NoSolverAvailableError("%s '%s' is not available" % \
                                             (solver_type, name))

            if logic is not None and \
               name not in self._filter_solvers(solver_list, logic=logic):
                raise NoSolverAvailableError("%s '%s' does not support logic %s"%
                                             (solver_type, name, logic))

            SolverClass = solver_list[name]
            if logic is None:
                try:
                    logic = most_generic_logic(SolverClass.LOGICS)
                except NoLogicAvailableError:
                    if default_logic in SolverClass.LOGICS:
                        logic = default_logic
                    else:
                        raise NoLogicAvailableError("Cannot automatically select a logic")

            closer_logic = get_closer_logic(SolverClass.LOGICS, logic)

            return SolverClass, closer_logic

        if logic is None:
            logic = default_logic

        solvers = self._filter_solvers(solver_list, logic=logic)

        if solvers is not None and len(solvers) > 0:
            # Pick the first solver based on preference list
            SolverClass = self._pick_favorite(preference_list, solver_list, solvers)
            closer_logic = get_closer_logic(SolverClass.LOGICS, logic)
            return SolverClass, closer_logic

        else:
            raise NoSolverAvailableError("No %s is available for logic %s" %
                                         (solver_type, logic))


    def _pick_favorite(self, preference_list, solver_list, solvers):
        for candidate in preference_list:
            if candidate in solvers:
                return solver_list[candidate]
        raise NoSolverAvailableError(
            "Cannot find a matching solver in the preference list: %s " % solvers)


    def add_generic_solver(self, name, args, logics, unsat_core_support=False):
        from pysmt.smtlib.solver import SmtLibSolver
        if name in self._all_solvers:
            raise SolverRedefinitionError("Solver %s already defined" % name)
        self._generic_solvers[name] = (args, logics)
        solver = partial(SmtLibSolver, args, LOGICS=logics)
        solver.LOGICS = logics
        solver.UNSAT_CORE_SUPPORT = unsat_core_support
        self._all_solvers[name] = solver
        # Extend preference list accordingly
        self.solver_preference_list.append(name)

    def is_generic_solver(self, name):
        return name in self._generic_solvers

    def get_generic_solver_info(self, name):
        return self._generic_solvers[name]

    def _get_available_solvers(self):
        installed_solvers = {}
        self._all_solvers = {}
        self._all_unsat_core_solvers = {}

        try:
            from pysmt.solvers.z3 import Z3Solver
            installed_solvers['z3'] = Z3Solver
        except SolverAPINotFound:
            pass

        try:
            from pysmt.solvers.msat import MathSAT5Solver
            installed_solvers['msat'] = MathSAT5Solver
        except SolverAPINotFound:
            pass

        try:
            from pysmt.solvers.cvc4 import CVC4Solver
            installed_solvers['cvc4'] = CVC4Solver
        except SolverAPINotFound:
            pass

        try:
            from pysmt.solvers.yices import YicesSolver
            installed_solvers['yices'] = YicesSolver
        except SolverAPINotFound:
            pass

        try:
            from pysmt.solvers.bdd import BddSolver
            installed_solvers['bdd'] = BddSolver
        except SolverAPINotFound:
            pass

        try:
            from pysmt.solvers.pico import PicosatSolver
            installed_solvers['picosat'] = PicosatSolver
        except SolverAPINotFound:
            pass

        try:
            from pysmt.solvers.btor import BoolectorSolver
            installed_solvers['btor'] = BoolectorSolver
        except SolverAPINotFound:
            pass

        # If ENV_SOLVER_LIST is set, only a subset of the installed
        # solvers will be available.
        if ENV_SOLVER_LIST is not None:
            for s in ENV_SOLVER_LIST:
                try:
                    v = installed_solvers[s]
                    self._all_solvers[s] = v
                except KeyError:
                    pass
        else:
            self._all_solvers = installed_solvers

        for k,s in self._all_solvers.items():
            try:
                if s.UNSAT_CORE_SUPPORT:
                    self._all_unsat_core_solvers[k] = s
            except AttributeError:
                pass


    def _get_available_qe(self):
        self._all_qelims = {}

        try:
            from pysmt.solvers.z3 import Z3QuantifierEliminator
            self._all_qelims['z3'] = Z3QuantifierEliminator
        except SolverAPINotFound:
            pass

        try:
            from pysmt.solvers.msat import (MSatFMQuantifierEliminator,
                                            MSatLWQuantifierEliminator)
            self._all_qelims['msat_fm'] = MSatFMQuantifierEliminator
            self._all_qelims['msat_lw'] = MSatLWQuantifierEliminator
        except SolverAPINotFound:
            pass

        try:
            from pysmt.solvers.bdd import BddQuantifierEliminator
            self._all_qelims['bdd'] = BddQuantifierEliminator
        except SolverAPINotFound:
            pass

        # Pure-python always present
        self._all_qelims['shannon'] = ShannonQuantifierEliminator
        self._all_qelims['selfsub'] = SelfSubstitutionQuantifierEliminator

    def _get_available_interpolators(self):
        self._all_interpolators = {}

        try:
            from pysmt.solvers.msat import MSatInterpolator
            self._all_interpolators['msat'] = MSatInterpolator
        except SolverAPINotFound:
            pass


    def set_solver_preference_list(self, preference_list):
        """Defines the order in which to pick the solvers.

        The list is not required to contain all the solvers. It is
        possible to define a subsets of the solvers, or even just
        one. The impact of this, is that the solver will never be
        selected automatically. Note, however, that the solver can
        still be selected by calling it by name.
        """
        assert preference_list is not None
        assert len(preference_list) > 0
        self.solver_preference_list = preference_list


    def set_qelim_preference_list(self, preference_list):
        """Defines the order in which to pick the solvers."""
        assert preference_list is not None
        assert len(preference_list) > 0
        self.qelim_preference_list = preference_list


    def set_interpolation_preference_list(self, preference_list):
        """Defines the order in which to pick the solvers."""
        assert preference_list is not None
        assert len(preference_list) > 0
        self.interpolation_preference_list = preference_list


    def _filter_solvers(self, solver_list, logic=None):
        """
        Returns a dict <solver_name, solver_class> including all and only
        the solvers directly or indirectly supporting the given logic.
        A solver supports a logic if either the given logic is
        declared in the LOGICS class field or if a logic subsuming the
        given logic is declared in the LOGICS class field.

        If logic is None, the map will contain all the known solvers
        """
        res = {}
        if logic is not None:
            for s, v in solver_list.items():
                for l in v.LOGICS:
                    if logic <= l:
                        res[s] = v
                        break
            return res
        else:
            solvers = solver_list

        return solvers


    def all_solvers(self, logic=None):
        """
        Returns a dict <solver_name, solver_class> including all and only
        the solvers directly or indirectly supporting the given logic.
        A solver supports a logic if either the given logic is
        declared in the LOGICS class field or if a logic subsuming the
        given logic is declared in the LOGICS class field.

        If logic is None, the map will contain all the known solvers
        """
        return self._filter_solvers(self._all_solvers, logic=logic)


    def has_solvers(self, logic=None):
        """
        Returns true if self.all_solvers(logic) is non-empty
        """
        return len(self.all_solvers(logic=logic)) > 0


    def all_quantifier_eliminators(self, logic=None):
        """Returns a dict <qelim_name, qelim_class> including all and only the
        quantifier eliminators directly or indirectly supporting the
        given logic.  A qelim supports a logic if either the given
        logic is declared in the LOGICS class field or if a logic
        subsuming the given logic is declared in the LOGICS class
        field.

        If logic is None, the map will contain all the known
        quantifier eliminators
        """
        return self._filter_solvers(self._all_qelims, logic=logic)


    def all_unsat_core_solvers(self, logic=None):
        """
        Returns a dict <solver_name, solver_class> including all and only
        the solvers supporting unsat core extraction and directly or
        indirectly supporting the given logic.  A solver supports a
        logic if either the given logic is declared in the LOGICS
        class field or if a logic subsuming the given logic is
        declared in the LOGICS class field.

        If logic is None, the map will contain all the known solvers
        """
        return self._filter_solvers(self._all_unsat_core_solvers, logic=logic)

    def all_interpolators(self, logic=None):
        """
        Returns a dict <solver_name, solver_class> including all and only
        the solvers supporting interpolation and directly or
        indirectly supporting the given logic.  A solver supports a
        logic if either the given logic is declared in the LOGICS
        class field or if a logic subsuming the given logic is
        declared in the LOGICS class field.

        If logic is None, the map will contain all the known solvers
        """
        return self._filter_solvers(self._all_interpolators, logic=logic)



    ##
    ## Wrappers: These functions are exported in shortcuts
    ##
    def Solver(self, name=None, logic=None, **options):
        return self.get_solver(name=name,
                               logic=logic,
                               **options)

    def UnsatCoreSolver(self, name=None, logic=None,
                        unsat_cores_mode="all", **options):
        return self.get_unsat_core_solver(name=name,
                                          logic=logic,
                                          unsat_cores_mode=unsat_cores_mode,
                                          **options)

    def QuantifierEliminator(self, name=None, logic=None):
        return self.get_quantifier_eliminator(name=name, logic=logic)

    def Interpolator(self, name=None, logic=None):
        return self.get_interpolator(name=name, logic=logic)

    def is_sat(self, formula, solver_name=None, logic=None, portfolio=None):
        if logic is None or logic == AUTO_LOGIC:
            logic = get_logic(formula, self.environment)
        if portfolio is not None:
            solver = Portfolio(solvers_set=portfolio,
                               environment=self.environment,
                               logic=logic,
                               generate_models=False, incremental=False)
        else:
            solver = self.Solver(name=solver_name, logic=logic,
                                 generate_models=False, incremental=False)
        with solver:
            return solver.is_sat(formula)

    def get_model(self, formula, solver_name=None, logic=None):
        if logic is None or logic == AUTO_LOGIC:
            logic = get_logic(formula, self.environment)
        with self.Solver(name=solver_name, logic=logic,
                         generate_models=True,
                         incremental=False) as solver:
            solver.add_assertion(formula)
            if solver.solve():
                return solver.get_model()
            return None

    def get_implicant(self, formula, solver_name=None,
                      logic=None):
        mgr = self.environment.formula_manager
        if logic is None or logic == AUTO_LOGIC:
            logic = get_logic(formula, self.environment)

        with self.Solver(name=solver_name, logic=logic) \
             as solver:
            solver.add_assertion(formula)
            check = solver.solve()
            if not check:
                return None
            else:
                model = solver.get_model()
                atoms = formula.get_atoms()
                res = []
                for a in atoms:
                    fv = a.get_free_variables()
                    if any(v in model for v in fv):
                        if solver.get_value(a).is_true():
                            res.append(a)
                        else:
                            assert solver.get_value(a).is_false()
                            res.append(mgr.Not(a))
                return mgr.And(res)

    def get_unsat_core(self, clauses, solver_name=None, logic=None):
        if logic is None or logic == AUTO_LOGIC:
            logic = get_logic(self.environment.formula_manager.And(clauses),
                              self.environment)

        with self.UnsatCoreSolver(name=solver_name, logic=logic) \
             as solver:
            for c in clauses:
                solver.add_assertion(c)
            check = solver.solve()
            if check:
                return None

            return solver.get_unsat_core()

    def is_valid(self, formula, solver_name=None, logic=None, portfolio=None):
        if logic is None or logic == AUTO_LOGIC:
            logic = get_logic(formula, self.environment)
        if portfolio is not None:
            solver = Portfolio(solvers_set=portfolio,
                               environment=self.environment,
                               logic=logic,
                               generate_models=False, incremental=False)
        else:
            solver = self.Solver(name=solver_name, logic=logic,
                                 generate_models=False, incremental=False)
        with solver:
            return solver.is_valid(formula)

    def is_unsat(self, formula, solver_name=None, logic=None, portfolio=None):
        if logic is None or logic == AUTO_LOGIC:
            logic = get_logic(formula, self.environment)
        if portfolio is not None:
            solver = Portfolio(solvers_set=portfolio,
                               environment=self.environment,
                               logic=logic,
                               generate_models=False, incremental=False)
        else:
            solver = self.Solver(name=solver_name, logic=logic,
                                 generate_models=False, incremental=False)
        with solver:
            return solver.is_unsat(formula)

    def qelim(self, formula, solver_name=None, logic=None):
        if logic is None or logic == AUTO_LOGIC:
            logic = get_logic(formula, self.environment)

        with self.QuantifierEliminator(name=solver_name, logic=logic) as qe:
            return qe.eliminate_quantifiers(formula)


    def binary_interpolant(self, formula_a, formula_b,
                           solver_name=None, logic=None):
        if logic is None or logic == AUTO_LOGIC:
            _And = self.environment.formula_manager.And
            logic = get_logic(_And(formula_a, formula_b))

        with self.Interpolator(name=solver_name, logic=logic) as itp:
            return itp.binary_interpolant(formula_a, formula_b)


    def sequence_interpolant(self, formulas, solver_name=None, logic=None):
        if logic is None or logic == AUTO_LOGIC:
            _And = self.environment.formula_manager.And
            logic = get_logic(_And(formulas))

        with self.Interpolator(name=solver_name, logic=logic) as itp:
            return itp.sequence_interpolant(formulas)


    @property
    def default_logic(self):
        return self._default_logic

    @default_logic.setter
    def default_logic(self, value):
        self._default_logic = value

    @property
    def default_qe_logic(self):
        return self._default_qe_logic

    @default_qe_logic.setter
    def default_qe_logic(self, value):
        self._default_qe_logic = value

# EOC Factory

# Check if we have a restriction on which solvers to make available in
# the current System Environment
#
# If PYSMT_SOLVER is "all" or unset, keep the Default preference list
#
# If PYSMT_SOLVER is "None" (literal None), the preference list will be empty
#
# Otherwise PYSMT_SOLVER is treated as  a comma-separated list: e.g.
#   "msat, z3, cvc4"
#
import os
ENV_SOLVER_LIST = os.environ.get("PYSMT_SOLVER")
if ENV_SOLVER_LIST is not None:
    if ENV_SOLVER_LIST.lower() == "all":
        ENV_SOLVER_LIST = None
    elif ENV_SOLVER_LIST.lower() == "none":
        ENV_SOLVER_LIST = []
    else:
        # E.g. "msat, z3"
        ENV_SOLVER_LIST = [s.strip() \
                           for s in ENV_SOLVER_LIST.lower().split(",")]
