"""
The rule class is used for a specific application of a strategy on a tiling.
This is not something the user should implement, as it is just a wrapper for
calling the Strategy class and storing its results.

A CombinatorialSpecification is (more or less) a set of Rule.
"""
import abc
from functools import partial
from typing import (
    TYPE_CHECKING,
    Any,
    Callable,
    Dict,
    Generic,
    Iterator,
    List,
    Optional,
    Sequence,
    Tuple,
    cast,
)

from sympy import Eq, Function

from ..combinatorial_class import CombinatorialClassType, CombinatorialObjectType
from ..exception import SanityCheckFailure, StrategyDoesNotApply
from .constructor import Constructor, DisjointUnion

if TYPE_CHECKING:
    from .strategy import (
        AbstractStrategy,
        Strategy,
        VerificationStrategy,
    )
    from .strategy_pack import StrategyPack


__all__ = ("Rule", "VerificationRule")


class AbstractRule(abc.ABC, Generic[CombinatorialClassType, CombinatorialObjectType]):
    """
    An instance of Rule is created by the __call__ method of strategy.
    """

    def __init__(
        self,
        strategy: "AbstractStrategy[CombinatorialClassType, CombinatorialObjectType]",
        comb_class: CombinatorialClassType,
        children: Optional[Tuple[CombinatorialClassType, ...]] = None,
    ):
        self.comb_class = comb_class
        self._strategy = strategy
        self.count_cache: Dict[Any, int] = {}
        self.obj_cache: Dict[Any, List[CombinatorialObjectType]] = {}
        self.subrecs: Optional[Tuple[Callable[..., int], ...]] = None
        self.subgenerators: Optional[
            Tuple[Callable[..., Iterator[CombinatorialObjectType]], ...]
        ] = (None)
        self.subsamplers: Optional[Tuple[Callable[..., CombinatorialObjectType], ...]]
        self._children = children
        self._non_empty_children: Optional[Tuple[CombinatorialClassType, ...]] = None

    @property
    def strategy(
        self,
    ) -> "AbstractStrategy[CombinatorialClassType, CombinatorialObjectType]":
        return self._strategy

    @property
    def ignore_parent(self) -> bool:
        """
        Return True if it is not worth expanding the parent/comb_class if
        this rule is found.
        """
        return self._strategy.ignore_parent

    @property
    def inferrable(self) -> bool:
        """
        Return True if the children could change using inferral strategies.
        """
        return self._strategy.inferrable

    @property
    def possibly_empty(self) -> bool:
        """
        Return True if it is possible that a child is empty.
        """
        return self._strategy.possibly_empty

    @property
    def workable(self) -> bool:
        """
        Return True if the children can expanded using other strategies.
        """
        return self._strategy.workable

    def set_subrecs(
        self,
        get_subrule: Callable[
            [CombinatorialClassType],
            "AbstractRule[CombinatorialClassType, CombinatorialObjectType]",
        ],
    ) -> None:
        """
        In general a rule comes in the context of a set of rules, e.g. in a
        CombinatorialSpecification. In order to count, generate, etc using the
        rule we must set the subfunctions that the children refer to.
        """
        self.subrecs = tuple(
            get_subrule(child).count_objects_of_size for child in self.children
        )
        self.subgenerators = tuple(
            get_subrule(child).generate_objects_of_size for child in self.children
        )
        self.subsamplers = tuple(
            get_subrule(child).random_sample_object_of_size for child in self.children
        )

    @property
    def children(self) -> Tuple[CombinatorialClassType, ...]:
        """
        Return the tuple of CombinatorialClass that are found by applying the
        decomposition function of the strategy to the parent.
        """
        if self._children is None:
            self._children = self._strategy.decomposition_function(self.comb_class)
            if self._children is None:
                raise StrategyDoesNotApply("{} does not apply".format(self._strategy))
        return self._children

    @property
    def formal_step(self) -> str:
        """
        Return a string with a short explanation about the rule.
        """
        return self._strategy.formal_step()

    @abc.abstractmethod
    def is_equivalence(self) -> bool:
        """
        Returns True if the rule is an equivalence.
        """

    def non_empty_children(self) -> Tuple[CombinatorialClassType, ...]:
        """
        Return the tuple of non-empty combinatorial classes that are found
        by applying the decomposition function.
        """
        if self._non_empty_children is None:
            self._non_empty_children = tuple(
                child for child in self.children if not child.is_empty()
            )
        return self._non_empty_children

    @abc.abstractmethod
    def count_objects_of_size(self, n: int, **parameters: int) -> int:
        """
        The function count the objects with respect to the parameters. The
        result is cached.
        """

    @abc.abstractmethod
    def get_equation(
        self, get_function: Callable[[CombinatorialClassType], Function]
    ) -> Eq:
        """
        Return the equation for the (ordinary) generating function.
        """

    @abc.abstractmethod
    def generate_objects_of_size(
        self, n: int, **parameters: int
    ) -> Iterator[CombinatorialObjectType]:
        """
        Generate the objects by using the underlying bijection between the
        parent and children.
        """

    @abc.abstractmethod
    def random_sample_object_of_size(
        self, n: int, **parameters: int
    ) -> CombinatorialObjectType:
        """Return a random objects of the give size."""

    def sanity_check(self, n: int, **parameters: int) -> bool:
        """
        Sanity check that this is a valid rule.

        Raise a SanityCheckFailure error if the sanity_check fails.
        """

        if isinstance(self, VerificationRule):
            # TODO: test more thoroughly
            return True

        def brute_force_count(
            comb_class: CombinatorialClassType, n: int, **parameters
        ) -> int:
            assert set(comb_class.extra_parameters) == set(
                parameters
            ), f"{comb_class.extra_parameters, set(parameters)}"
            return len(list(comb_class.objects_of_size(n, **parameters)))

        def brute_force_generation(
            comb_class: CombinatorialClassType, n: int, **parameters: int
        ) -> Iterator[CombinatorialObjectType]:
            assert set(comb_class.extra_parameters) == set(
                parameters
            ), f"{comb_class.extra_parameters, set(parameters)}"
            yield from comb_class.objects_of_size(n, **parameters)

        actual_count = brute_force_count(self.comb_class, n, **parameters)
        temprec = self.subrecs
        self.subrecs = tuple(
            partial(brute_force_count, child) for child in self.children
        )
        rule_count = self.count_objects_of_size(n, **parameters)
        self.subrecs = temprec
        params_str = ", ".join(
            [f"n = {n}"] + [f"{p} = {v}" for p, v in parameters.items()]
        )
        if actual_count != rule_count:
            raise SanityCheckFailure(
                f"The following rule failed sanity check:\n"
                f"{self}\n"
                f"Failed with parameters:\n"
                f"{params_str}\n"
                f"The actual count is {actual_count}.\n"
                f"The rule count is {rule_count}.",
            )
        if any(
            comb_class.extra_parameters
            for comb_class in [self.comb_class, *self.children]
        ):
            # Can't sanity check generation of objects for classes with extra
            # TODO test more thoroughly
            return True
        tempgen = self.subgenerators
        self.subgenerators = tuple(
            partial(brute_force_generation, child) for child in self.children
        )
        try:
            rule_objects = set(list(self.generate_objects_of_size(n, **parameters)))
        except NotImplementedError:
            # Skipping testing rules that have not implemented object generation.
            return True
        self.subgenerators = tempgen
        actual_objects = set(
            list(brute_force_generation(self.comb_class, n, **parameters))
        )
        if actual_objects != rule_objects:
            raise SanityCheckFailure(
                f"The following rule failed sanity check:\n"
                f"{self}\n"
                f"Failed with parameters:\n"
                f"{params_str}\n"
                f"The rule generated the wrong objects."
            )
        return True

    def __eq__(self, other: object) -> bool:
        if not isinstance(other, AbstractRule):
            return NotImplemented
        return (
            isinstance(other, self.__class__)
            and self.comb_class == other.comb_class
            and self._strategy == other._strategy
        )

    def __str__(self) -> str:
        def frontpad(res, height):
            n = max(len(s) for s in res)
            for _ in range(height - len(res)):
                res.append(" " * n)

        def backpad(res):
            n = max(len(s) for s in res)
            for i, s in enumerate(res):
                res[i] = s + " " * (n - len(s))

        def join(res, new):
            backpad(new)
            backpad(res)
            frontpad(res, len(new))
            for i, s in enumerate(new):
                res[i] += s

        res = str(self.comb_class).split("\n")
        backpad(res)
        if isinstance(self, Rule):
            children = [str(child).split("\n") for child in self.children]
            symbol_height = min(1, len(res) - 1)
            eq_symbol = (
                ["     " for i in range(symbol_height)]
                + ["  {}  ".format(self.strategy.get_eq_symbol())]
                + ["     " for i in range(symbol_height)]
            )
            join(res, eq_symbol)
            join(res, children[0])
            if len(children) > 1:
                op_symbol = (
                    ["     " for i in range(symbol_height)]
                    + ["  {}  ".format(self.strategy.get_op_symbol())]
                    + ["     " for i in range(symbol_height)]
                )
                for child in children[1:]:
                    join(res, op_symbol)
                    join(res, child)
        return f"{self.formal_step}\n" + "\n".join(x for x in res)


class Rule(AbstractRule[CombinatorialClassType, CombinatorialObjectType]):
    def __init__(
        self,
        strategy: "Strategy[CombinatorialClassType, CombinatorialObjectType]",
        comb_class: CombinatorialClassType,
        children: Optional[Tuple[CombinatorialClassType, ...]] = None,
    ):
        super().__init__(strategy, comb_class, children)
        self._constructor: Optional[Constructor] = None

    @property
    def strategy(self) -> "Strategy[CombinatorialClassType, CombinatorialObjectType]":
        return cast(
            "Strategy[CombinatorialClassType, CombinatorialObjectType]",
            super().strategy,
        )

    @property
    def constructor(
        self,
    ) -> Constructor[CombinatorialClassType, CombinatorialObjectType]:
        """
        Return the constructor, that contains all the information about how to
        count/generate objects from the rule.
        """
        if self._constructor is None:
            self._constructor = self.strategy.constructor(
                self.comb_class, self.children
            )
            if self._constructor is None:
                raise StrategyDoesNotApply("{} does not apply".format(self.strategy))
        return self._constructor

    def is_equivalence(self):
        return self.strategy.can_be_equivalent() and len(self.non_empty_children()) == 1

    def backward_map(
        self, objs: Tuple[Optional[CombinatorialObjectType], ...]
    ) -> CombinatorialObjectType:
        """
        This encodes the backward map of the underlying bijection that the
        strategy implies.
        """
        return self.strategy.backward_map(self.comb_class, objs, self.children)

    def forward_map(
        self, obj: CombinatorialObjectType
    ) -> Tuple[Optional[CombinatorialObjectType], ...]:
        """
        This encodes the forward map of the underlying bijection that the
        strategy implies.
        """
        return self.strategy.forward_map(self.comb_class, obj, self.children)

    def to_equivalence_rule(self) -> "EquivalenceRule":
        """
        Return the reverse rule. At this stage, reverse rules can only be
        created for equivalence rules.
        """
        assert (
            self.is_equivalence()
        ), f"EquivalenceRule can only be created for equivalence rules\n{self}"
        return EquivalenceRule(self)

    def to_reverse_rule(self) -> "ReverseRule":
        """
        Return the reverse rule. At this stage, reverse rules can only be
        created for equivalence rules.
        """
        assert (
            self.is_equivalence()
        ), "reverse rule can only be created for equivalence rules"
        return ReverseRule(self)

    def count_objects_of_size(self, n: int, **parameters: int) -> int:
        """
        The function count the objects with respect to the parameters. The
        result is cached.
        """
        key = (n,) + tuple(sorted(parameters.items()))
        assert sorted(["n"] + list(parameters)) == sorted(
            ("n",) + self.comb_class.extra_parameters
        ), (
            "parameters did not match comb_class for the rule \n{}\nparameters "
            "given: {}\n comb_class_parameters: {}".format(
                self, list(parameters), self.comb_class.extra_parameters,
            )
        )
        res = self.count_cache.get(key)
        if res is None:
            assert (
                self.subrecs is not None
            ), "you must call the set_subrecs function first"
            try:
                res = self.constructor.get_recurrence(self.subrecs, n, **parameters)
            except AssertionError:
                raise ValueError(f"issue with rule:\n {self}")
            self.count_cache[key] = res
            # THE FOLLOWING CODE SNIPPET IS FOR DEBUGGING PURPOSES
        #     if self.comb_class.extra_parameters:
        #         print(self)
        #         print("n =", n, parameters)
        #         if hasattr(self.constructor, "extra_parameters"):
        #             print("parent->child params:", self.constructor.extra_parameters)
        #         if hasattr(self.constructor, "split_parameters"):
        #             print("parent->child params:", self.constructor.split_parameters)
        #         if hasattr(self.constructor, "zeroes"):
        #             print("zeroes:", self.constructor.zeroes)
        #         if hasattr(self.constructor, "get_extra_parameters"):
        #             print(
        #                 "parameters_passed:",
        #                 self.constructor.get_extra_parameters(n, **parameters),
        #             )
        #         fusion_attrs = [
        #             "extra_parameters",
        #             "reversed_extra_parameters",
        #             "fuse_parameter",
        #             "left_sided_parameters",
        #             "right_sided_parameters",
        #             "both_sided_parameters",
        #             "rec_function",
        #             "fusion_type",
        #             "predeterminable_left_right_points",
        #         ]
        #         for string in fusion_attrs:
        #             if hasattr(self.constructor, string):
        #                 print(string + ":", getattr(self.constructor, string))
        #         if isinstance(self.constructor, CartesianProduct):
        #             print(list(self.constructor._valid_compositions(n, **parameters)))
        #     print("result:", res)
        # assert res == len(list(self.comb_class.objects_of_size(n, **parameters))), (
        #     "counting failed for the rule \n{}\nparameters: n = {}, {}\n"
        #     "computed {}, actual {}".format(
        #         self,
        #         n,
        #         parameters,
        #         res,
        #         len(list(self.comb_class.objects_of_size(n, **parameters))),
        #     )
        # )
        return res

    def get_equation(
        self, get_function: Callable[[CombinatorialClassType], Function]
    ) -> Eq:
        """
        Return the equation for the (ordinary) generating function.
        """
        lhs_func = get_function(self.comb_class)
        rhs_funcs = tuple(get_function(comb_class) for comb_class in self.children)
        return self.constructor.get_equation(lhs_func, rhs_funcs)

    def generate_objects_of_size(
        self, n: int, **parameters: int
    ) -> Iterator[CombinatorialObjectType]:
        """
        Generate the objects by using the underlying bijection between the
        parent and children.
        """
        key = (n,) + tuple(sorted(parameters.items()))
        res = self.obj_cache.get(key)
        if res is not None:
            yield from res
            return
        assert (
            self.subgenerators is not None
        ), "you must call the set_subrecs function first"
        res = []
        for subobjs in self.constructor.get_sub_objects(
            self.subgenerators, n, **parameters
        ):
            obj = self.backward_map(subobjs)
            yield obj
            res.append(obj)
        self.obj_cache[key] = res

    def random_sample_object_of_size(
        self, n: int, **parameters: int
    ) -> CombinatorialObjectType:
        assert self.subrecs is not None, "you must call the set_subrecs function first"
        assert (
            self.subsamplers is not None
        ), "you must call the set_subrecs function first"
        total_count = self.count_objects_of_size(n=n, **parameters)
        subobjs = self.constructor.random_sample_sub_objects(
            total_count, self.subsamplers, self.subrecs, n, **parameters
        )
        obj = self.backward_map(subobjs)
        return obj


class EquivalenceRule(Rule[CombinatorialClassType, CombinatorialObjectType]):
    def __init__(self, rule: Rule):
        non_empty_children = rule.non_empty_children()
        assert rule.is_equivalence(), "not an equivalence rule: {}".format(str(rule))
        child = non_empty_children[0]
        super().__init__(rule.strategy, rule.comb_class, (child,))
        self.child_idx = rule.children.index(child)
        self.actual_children = rule.children
        self._constructor: Optional[DisjointUnion] = None

    @property
    def constructor(self,) -> DisjointUnion:
        """
        Return the constructor, that contains all the information about how to
        count/generate objects from the rule.
        """
        if self._constructor is None:
            original_constructor = cast(
                DisjointUnion,
                self.strategy.constructor(self.comb_class, self.actual_children),
            )
            assert isinstance(original_constructor, DisjointUnion)
            self._constructor = DisjointUnion(
                self.comb_class,
                self.children,
                (original_constructor.extra_parameters[self.child_idx],),
            )
        return self._constructor

    @staticmethod
    def is_equivalence() -> bool:
        return True

    @property
    def formal_step(self) -> str:
        return "{} but only the child at index {} is non-empty".format(
            self.strategy.formal_step(), self.child_idx
        )

    def backward_map(
        self, objs: Tuple[Optional[CombinatorialObjectType], ...]
    ) -> CombinatorialObjectType:
        actual_objs = tuple(
            objs[0] if i == self.child_idx else None
            for i in range(len(self.actual_children))
        )
        return self.strategy.backward_map(
            self.comb_class, actual_objs, self.actual_children
        )

    def forward_map(
        self, obj: CombinatorialObjectType
    ) -> Tuple[Optional[CombinatorialObjectType], ...]:
        return (
            self.strategy.forward_map(self.comb_class, obj, self.actual_children)[
                self.child_idx
            ],
        )


class EquivalencePathRule(Rule[CombinatorialClassType, CombinatorialObjectType]):
    """
    A class for shortening a chain of equivalence rules into a single Rule.
    """

    def __init__(self, rules: Sequence[Rule]):
        assert all(rule.is_equivalence() for rule in rules)
        super().__init__(rules[0].strategy, rules[0].comb_class, rules[-1].children)
        self.rules = rules
        self._constructor: Optional[DisjointUnion] = None

    @property
    def constructor(self) -> DisjointUnion:
        if self._constructor is None:
            extra_parameters: Dict[str, str] = {
                k: k for k in self.comb_class.extra_parameters
            }
            for rule in self.rules:
                original_constructor = rule.constructor
                assert isinstance(original_constructor, DisjointUnion)
                rules_parameters = original_constructor.extra_parameters[0]
                extra_parameters = {
                    parent_var: rules_parameters[child_var]
                    for parent_var, child_var in extra_parameters.items()
                    if child_var in rules_parameters
                }
            fixed_values = {
                k: 0
                for k in self.children[0].extra_parameters
                if k not in extra_parameters.values()
            }
            self._constructor = DisjointUnion(
                self.comb_class, self.children, (extra_parameters,), (fixed_values,)
            )
        return self._constructor

    @staticmethod
    def is_equivalence() -> bool:
        return True

    @property
    def formal_step(self) -> str:
        return ", then ".join(rule.formal_step for rule in self.rules)

    def eqv_path_rules(self) -> List[Tuple[CombinatorialClassType, Rule]]:
        """
        Returns the list of (parent, rule) pairs that make up the equivalence
        path.
        """
        eqv_path_rules = []
        curr = self.comb_class
        for rule in self.rules:
            eqv_path_rules.append((curr, rule))
            curr = rule.children[0]
        return eqv_path_rules

    def backward_map(
        self, objs: Tuple[Optional[CombinatorialObjectType], ...]
    ) -> CombinatorialObjectType:
        res = cast(Tuple[CombinatorialObjectType], objs)
        for rule in reversed(self.rules):
            res = (rule.backward_map(res),)
        return res[0]

    def forward_map(
        self, obj: CombinatorialObjectType
    ) -> Tuple[Optional[CombinatorialObjectType], ...]:
        res = cast(CombinatorialObjectType, obj)
        for rule in reversed(self.rules):
            res = cast(CombinatorialObjectType, rule.forward_map(res)[0])
        return (res,)

    def __eq__(self, other) -> bool:
        return super().__eq__(other) and [r.strategy for r in self.rules] == [
            r.strategy for r in other.rules
        ]

    def __str__(self) -> str:
        def frontpad(res, height):
            n = max(len(s) for s in res)
            for _ in range(height - len(res)):
                res.append(" " * n)

        def backpad(res):
            n = max(len(s) for s in res)
            for i, s in enumerate(res):
                res[i] = s + " " * (n - len(s))

        def join(res, new):
            backpad(new)
            backpad(res)
            frontpad(res, len(new))
            for i, s in enumerate(new):
                res[i] += s

        res = str(self.comb_class).split("\n")
        backpad(res)
        comb_classes = [str(rule.children[0]).split("\n") for rule in self.rules]
        symbol_height = min(1, len(res) - 1)
        eq_symbol = (
            ["     " for i in range(symbol_height)]
            + ["  {}  ".format("=")]
            + ["     " for i in range(symbol_height)]
        )
        for comb_class in comb_classes:
            join(res, eq_symbol)
            join(res, comb_class)
        return "{}\n".format(self.formal_step) + "\n".join(x for x in res)


class ReverseRule(Rule[CombinatorialClassType, CombinatorialObjectType]):
    """
    A class for creating the reverse of an equivalence rule.
    """

    def __init__(self, rule: Rule):
        assert (
            rule.is_equivalence()
        ), "reversing a rule only works for equivalence rules"
        super().__init__(rule.strategy, rule.children[0], (rule.comb_class,))
        self.original_rule = rule
        self._constructor: Optional[DisjointUnion] = None

    @property
    def constructor(self) -> DisjointUnion:
        if self._constructor is None:
            original_constructor = self.original_rule.constructor
            assert isinstance(original_constructor, DisjointUnion), (
                "reverse rule coming from non disjoint union strategy - "
                "you'll need to update the ReverseRule constructor!"
            )
            flipped_extra_params = {
                b: a for a, b in original_constructor.extra_parameters[0].items()
            }
            fixed_values = {
                k: 0
                for k in self.children[0].extra_parameters
                if k not in flipped_extra_params.values()
            }
            self._constructor = DisjointUnion(
                self.comb_class, self.children, (flipped_extra_params,), (fixed_values,)
            )
        return self._constructor

    @staticmethod
    def is_equivalence() -> bool:
        return True

    @property
    def formal_step(self) -> str:
        return "reverse of '{}'".format(self.strategy.formal_step())

    def backward_map(
        self, objs: Tuple[Optional[CombinatorialObjectType], ...]
    ) -> CombinatorialObjectType:
        return cast(CombinatorialObjectType, self.original_rule.forward_map(objs[0])[0])

    def forward_map(
        self, obj: CombinatorialObjectType
    ) -> Tuple[Optional[CombinatorialObjectType], ...]:
        return (self.original_rule.backward_map((obj,)),)


class VerificationRule(AbstractRule[CombinatorialClassType, CombinatorialObjectType]):
    """
    A class for verification rules. In particular, the children will be an
    empty tuple if it applies, else None.
    """

    def __init__(
        self,
        strat: "VerificationStrategy[CombinatorialClassType,CombinatorialObjectType]",
        comb_class: CombinatorialClassType,
        children: Optional[Tuple[CombinatorialClassType, ...]] = None,
    ):
        assert not children
        super().__init__(strat, comb_class, children)

    @property
    def strategy(
        self,
    ) -> "VerificationStrategy[CombinatorialClassType, CombinatorialObjectType]":
        return cast(
            "VerificationStrategy[CombinatorialClassType, CombinatorialObjectType]",
            super().strategy,
        )

    @staticmethod
    def is_equivalence() -> bool:
        return False

    def count_objects_of_size(self, n: int, **parameters: int) -> int:
        key = (n,) + tuple(sorted(parameters.items()))
        assert set(parameters) == set(self.comb_class.extra_parameters)
        res = self.count_cache.get(key)
        if res is None:
            res = self.strategy.count_objects_of_size(self.comb_class, n, **parameters)
            self.count_cache[key] = res
        # # THE FOLLOWING CODE SNIPPET IS FOR DEBUGGING PURPOSES
        # assert res == len(list(self.comb_class.objects_of_size(n, **parameters))), (
        #     "counting failed for the rule \n{}\nparameters: n = {}, {}\n"
        #     "computed {}, actual {}".format(
        #         self,
        #         n,
        #         parameters,
        #         res,
        #         len(list(self.comb_class.objects_of_size(n, **parameters))),
        #     )
        # )
        return res

    def pack(self) -> "StrategyPack":
        return self.strategy.pack(self.comb_class)

    def get_equation(
        self,
        get_function: Callable[[CombinatorialClassType], Function],
        funcs: Optional[Dict[CombinatorialClassType, Function]] = None,
    ) -> Eq:
        lhs_func = get_function(self.comb_class)
        return Eq(lhs_func, self.strategy.get_genf(self.comb_class, funcs))

    def generate_objects_of_size(
        self, n: int, **parameters: int
    ) -> Iterator[CombinatorialObjectType]:
        key = (n,) + tuple(sorted(parameters.items()))
        res = self.obj_cache.get(key)
        if res is not None:
            yield from res
            return
        res = []
        for obj in self.strategy.generate_objects_of_size(
            self.comb_class, n, **parameters
        ):
            yield obj
            res.append(obj)
        self.obj_cache[key] = res

    def random_sample_object_of_size(
        self, n: int, **parameters: int
    ) -> CombinatorialObjectType:
        return self.strategy.random_sample_object_of_size(
            self.comb_class, n, **parameters
        )
