"""The Answers object"""


import re
from collections import defaultdict

import clyngor
from clyngor import as_pyasp, parsing, utils


def naive_parsing_of_answer_set(answer_set:str, *, discard_quotes:bool=False, parse_int:bool=True, parse_args:bool=True) -> [(str, tuple)]:
    """Yield (pred, args), naively parsed from given answer set encoded as clingo output string.
    Some atoms may be missing. Some others may be poorly parsed."""
    # print('NAIVE_PARSING_OF_ANSWER_SET:', answer_set, f'\t keep_quotes={keep_quotes}, parse_int={parse_int}, parse_args={parse_args}')
    REG_ANSWER_SET = re.compile(r'([a-z_-][a-zA-Z0-9_]*|[0-9]+|"[^"]*")(\([^)]+\))?')

    for match in REG_ANSWER_SET.finditer(answer_set):
        pred, args = match.groups()
        assert args is None or (args.startswith('(') and args.endswith(')'))
        if args and parse_args:
            args = args[1:-1]  # remove surrounding parens
            if discard_quotes:
                args = utils.remove_arguments_quotes(args)
            args = tuple(
                (int(arg) if parse_int and
                 (arg[1:] if arg.startswith('-') else arg).isnumeric() else arg)
                for arg in args.split(',')
            ) if parse_args else args
        elif args:  # args should not be parsed
            args = args
        pred = int(pred) if pred.isnumeric() else pred  # handle
        yield pred, args or ()
        # print('\t>', pred, args)


class Answers:
    """Proxy to the solver, generated by solving methods like solve.solve
    or inline.ASP.

    Iterable on the answer sets generated by the solver.
    Also expose some answer set formatting tunning.

    """

    def __init__(self, answers:iter, command:str='', statistics:dict={},
                 *, decoders:iter=(), with_optimization:bool=False, on_end:callable=None, **kwargs):
        """Answer sets must be iterable of (predicate, args).

        decoders -- iterable of decoders to apply on ASP (see clyngor.decoder).
        with_optimization -- answers are read as ((predicate, args), optimization, optimality)
                             allowing to retrieve optimization data of the answers.
                             See also Answers.with_optimization property.
        on_end -- if callable, called when all answer sets are exhausted.

        """
        if not with_optimization:
            # emulate optimization with None value
            # TODO: verify that in absence of optimization, all answers are ALWAYS enumerated from 1 to n.
            answers = ((answer, None, False, idx) for idx, answer in enumerate(answers, start=1))
        self._answers = iter(answers)
        self._command = str(command or '')
        self._statistics = statistics  # will be updated by reading method
        self._decoders = tuple(decoders)
        self._first_arg_only = False
        self._group_atoms = False
        self._group_atoms_with_arity = False
        self._as_pyasp = False
        self._sorted = False
        self._keep_quotes = True
        # TODO: break api to add keep quotes instead of discard quotes.
        self._careful_parsing = False
        self._collapse_atoms= False
        self._collapse_args = True
        self._parse_int = True
        self._ignore_args = False
        self._with_optimization = False
        self._with_optimality = False
        self._with_answer_number = False
        self.__on_end = on_end or (lambda: None)

        for k, v in kwargs.items():
            if isinstance(getattr(self, '_' + k, None), bool):
                setattr(self, '_' + k, bool(value))
            elif k == 'discard_quotes':
                setattr(self, '_keep_quotes', not bool(value))

    def __del__(self):
        """Call the on_end function, avoiding a too-many-file-open error.

        That error happened in a recent project where clyngor
        was called thousands of times, without accessing all models,
        which prevented __iter__ method to ever call on_end function.
        Hence the use of __del__.

        """
        self.clean_resources()

    @property
    def command(self) -> str:  return self._command

    @property
    def with_optimization(self):
        """Yield (model, optimization) instead of just model"""
        self._with_optimization = True
        return self

    @property
    def with_optimality(self):
        """Yield (model, optimization, optimality) instead of just model"""
        self._with_optimization = True
        self._with_optimality = True
        return self

    @property
    def with_answer_number(self):
        """Yield (model, optimization, optimality, answer_number) instead of just model"""
        self._with_optimization = True
        self._with_optimality = True
        self._with_answer_number = True
        return self

    @property
    def first_arg_only(self):
        """Keep only the first argument, and do not enclose it in a tuple."""
        self._first_arg_only = True
        return self

    @property
    def by_predicate(self):
        """Group atoms by predicate. Answer sets are then dict with predicate
        as keys and collection of args as value."""
        self._group_atoms = True
        return self

    @property
    def by_arity(self):
        """Group atoms by predicate and arity. Answer sets are then dict with
        predicate/arity as keys and collection of args as value."""
        self._group_atoms = True
        self._group_atoms_with_arity = True
        return self

    @property
    def as_pyasp(self):
        """Return Term and TermSet object offering a pyasp-like interface"""
        self._as_pyasp = True
        return self

    @property
    def sorted(self):
        """Sort the atom (or the args when grouped)"""
        self._sorted = True
        return self

    @property
    def keep_quotes(self):
        """Keep the quotes of the string"""
        self._keep_quotes = True
        return self

    @property
    def discard_quotes(self):
        """Discard the quotes of the string"""
        self._keep_quotes = False
        return self

    @property
    def careful_parsing(self):
        """Use robust parser"""
        self._careful_parsing = True
        return self

    @property
    def atoms_as_string(self):
        """All atoms are encoded as ASP strings, left unparsed."""
        self._collapse_atoms = True
        self._collapse_args = True
        return self

    @property
    def int_not_parsed(self):
        """Do not parse the integer arguments, so if an atom have integers
        as arguments, they will be returned as string, not integers.

        """
        self._parse_int = False
        return self

    @property
    def parse_args(self):
        """Parse the arguments as well, so if an atom is argument of another
        one, it will be parsed as any atom instead of being understood
        as a string.

        Will use the robust parser.

        """
        self._careful_parsing = True  # needed to implement the collapse
        self._collapse_atoms = False
        self._collapse_args = False
        return self

    @property
    def no_arg(self):
        """Do not parse arguments, and discard/ignore them.

        """
        self._ignore_args = True
        return self


    def __next__(self):
        return next(iter(self))

    def __iter__(self):
        """Yield answer sets"""
        for answer_set, optimization, optimality, answer_number in self._answers:
            answer_set = tuple(self._parse_answer(answer_set))
            parsed = self._format(answer_set)
            if self._with_answer_number:
                yield parsed, optimization, optimality, answer_number
            elif self._with_optimality:
                yield parsed, optimization, optimality
            elif self._with_optimization:
                yield parsed, optimization
            else:
                yield parsed
        self.clean_resources()

    def clean_resources(self):
        self.__on_end()
        self.__on_end = lambda: None  # don't call it again


    def _parse_answer(self, answer_set:str) -> iter:
        """Yield atoms as (pred, args) from given answer set"""
        careful_parsing = self._careful_parsing or parsing.careful_parsing_required(answer_set)
        # keep_quotes = self._keep_quotes or not self._collapse_atoms or not self._as_pyasp
        discard_quotes = not (self._keep_quotes or self._collapse_atoms or self._as_pyasp)
        if isinstance(answer_set, str) and careful_parsing:
            # print('CAREFUL PARSING:', answer_set)
            # _keep_quotes is incompatible with atoms_as_string and as_pyasp.
            # atom_as_string: remove the quotes delimiting arguments.
            # as_pyasp: remove the quotes for the arguments.
            yield from parsing.Parser(
                self._collapse_atoms, self._collapse_args,
                discard_quotes,
                self._first_arg_only,
                parse_integer=self._parse_int
            ).parse_terms(answer_set)
        elif isinstance(answer_set, str):  # the good ol' split
            # print('THE GOOD OLD SPLIT:', f"keep_quotes={self._keep_quotes}  collapse_atoms={self._collapse_atoms}")
            yield from self.__finish_parsing(naive_parsing_of_answer_set(answer_set, discard_quotes=discard_quotes, parse_int=self._parse_int, parse_args=self._collapse_args or self._first_arg_only))
        elif isinstance(answer_set, (set, tuple)) and all(isinstance(atom, (str, int, tuple)) for atom in answer_set):  # already parsed
            # print('FROM SET OR TUPLE')
            if not self._parse_int:
                answer_set = utils.integers_to_string_atoms(answer_set)
            yield from self.__finish_parsing(answer_set)
        else:  # unknown format
            raise ValueError("unknow answer set format: {}, {}".format(type(answer_set), answer_set))

    def __finish_parsing(self, answer_set:[(str, tuple)]) -> [(str, tuple) or str]:
        """Modify (pred, args) atoms according to parsing options"""
        for pred, args in answer_set:
            if self._first_arg_only:
                args = [args[0]] if args else args
            if self._atoms_as_string:
                args = ('(' + ','.join(map(str, args)) + ')') if args else ''
                yield pred + args
            else:
                yield pred, args


    def _format(self, answer_set) -> dict or frozenset:
        """Perform the formatting of the answer set according to
        formatting options.

        answer_set -- iterable of (pred, args)

        """
        sorted_tuple = lambda it: tuple(sorted(it))
        builder = sorted_tuple if self._sorted else frozenset
        if self._atoms_as_string:  # special case
            return builder(answer_set)
        elif self._ignore_args:
            answer_set = (pred for pred, _ in answer_set)
            if self._group_atoms:
                return {pred: frozenset() for pred in answer_set}
            if self._as_pyasp:
                return as_pyasp.TermSet(as_pyasp.Atom.from_tuple_repr((pred, ())) for pred in answer_set)
            return builder(answer_set)
        elif self._first_arg_only:
            answer_set = builder((pred, args[0] if args else ())
                                   for pred, args in answer_set)
        else:
            answer_set = builder((pred, tuple(args))
                                   for pred, args in answer_set)
        # NB: as_pyasp flag behave differently if group_atoms is activated
        if self._group_atoms:
            mapping = defaultdict(set)
            for pred, args in answer_set:
                if self._as_pyasp:
                    args = as_pyasp.Atom.from_tuple_repr((pred, args))
                mapping[pred].add(args)
                if self._group_atoms_with_arity:
                    mapping[pred, len(args)].add(args)
                    mapping['{}/{}'.format(pred, str(len(args)))].add(args)
            if self._as_pyasp:
                return {pred: as_pyasp.TermSet(args) for pred, args in mapping.items()}
            else:
                return {pred: builder(args) for pred, args in mapping.items()}
        elif self._as_pyasp:
            return as_pyasp.TermSet(as_pyasp.Atom.from_tuple_repr(atom) for atom in answer_set)
        return answer_set


    @property
    def _atoms_as_string(self) -> bool:
        """Shortcut"""
        return self._collapse_atoms and self._collapse_args

    @property
    def statistics(self) -> dict:
        return dict(self._statistics)


class ClingoAnswers(Answers):
    """Proxy to the solver as called through the python clingo module.

    """
    def __init__(self, solver, statistics:callable=(lambda: {})):
        assert clyngor.have_clingo_module()
        super().__init__(self.__compute_answers(), with_optimization=True, command='[clingo module call]')
        self._solver = solver
        self._statistics = lambda s=solver: s.statistics
        assert callable(self._statistics)


    def __compute_answers(self):
        kwargs = {'yield_': True, 'async_': True}  # compat with 3.7
        with self._solver.solve(**kwargs) as models:
            for model in models:
                answer_set = set(utils.clingo_symbol_as_python_value(a)
                                 for a in model.symbols(shown=True))
                yield answer_set, model.cost, model.optimality_proven, model.number

    @property
    def statistics(self) -> dict:
        return self._statistics()