Adding all project files
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from ..structs import RequirementInformation
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from .abstract import AbstractResolver, Result
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from .criterion import Criterion
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from .exceptions import (
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InconsistentCandidate,
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RequirementsConflicted,
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ResolutionError,
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ResolutionImpossible,
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ResolutionTooDeep,
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ResolverException,
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)
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from .resolution import Resolution, Resolver
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__all__ = [
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"AbstractResolver",
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"Criterion",
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"InconsistentCandidate",
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"RequirementInformation",
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"RequirementsConflicted",
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"Resolution",
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"ResolutionError",
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"ResolutionImpossible",
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"ResolutionTooDeep",
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"Resolver",
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"ResolverException",
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"Result",
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]
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from __future__ import annotations
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import collections
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from typing import TYPE_CHECKING, Any, Generic, Iterable, Mapping, NamedTuple
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from ..structs import CT, KT, RT, DirectedGraph
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if TYPE_CHECKING:
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from ..providers import AbstractProvider
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from ..reporters import BaseReporter
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from .criterion import Criterion
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class Result(NamedTuple, Generic[RT, CT, KT]):
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mapping: Mapping[KT, CT]
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graph: DirectedGraph[KT | None]
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criteria: Mapping[KT, Criterion[RT, CT]]
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else:
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Result = collections.namedtuple("Result", ["mapping", "graph", "criteria"])
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class AbstractResolver(Generic[RT, CT, KT]):
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"""The thing that performs the actual resolution work."""
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base_exception = Exception
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def __init__(
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self,
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provider: AbstractProvider[RT, CT, KT],
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reporter: BaseReporter[RT, CT, KT],
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) -> None:
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self.provider = provider
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self.reporter = reporter
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def resolve(self, requirements: Iterable[RT], **kwargs: Any) -> Result[RT, CT, KT]:
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"""Take a collection of constraints, spit out the resolution result.
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This returns a representation of the final resolution state, with one
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guarenteed attribute ``mapping`` that contains resolved candidates as
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values. The keys are their respective identifiers.
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:param requirements: A collection of constraints.
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:param kwargs: Additional keyword arguments that subclasses may accept.
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:raises: ``self.base_exception`` or its subclass.
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"""
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raise NotImplementedError
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from __future__ import annotations
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from typing import Collection, Generic, Iterable, Iterator
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from ..structs import CT, RT, RequirementInformation
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class Criterion(Generic[RT, CT]):
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"""Representation of possible resolution results of a package.
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This holds three attributes:
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* `information` is a collection of `RequirementInformation` pairs.
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Each pair is a requirement contributing to this criterion, and the
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candidate that provides the requirement.
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* `incompatibilities` is a collection of all known not-to-work candidates
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to exclude from consideration.
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* `candidates` is a collection containing all possible candidates deducted
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from the union of contributing requirements and known incompatibilities.
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It should never be empty, except when the criterion is an attribute of a
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raised `RequirementsConflicted` (in which case it is always empty).
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.. note::
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This class is intended to be externally immutable. **Do not** mutate
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any of its attribute containers.
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"""
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def __init__(
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self,
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candidates: Iterable[CT],
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information: Collection[RequirementInformation[RT, CT]],
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incompatibilities: Collection[CT],
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) -> None:
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self.candidates = candidates
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self.information = information
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self.incompatibilities = incompatibilities
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def __repr__(self) -> str:
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requirements = ", ".join(
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f"({req!r}, via={parent!r})" for req, parent in self.information
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)
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return f"Criterion({requirements})"
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def iter_requirement(self) -> Iterator[RT]:
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return (i.requirement for i in self.information)
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def iter_parent(self) -> Iterator[CT | None]:
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return (i.parent for i in self.information)
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from __future__ import annotations
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from typing import TYPE_CHECKING, Collection, Generic
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from ..structs import CT, RT, RequirementInformation
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if TYPE_CHECKING:
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from .criterion import Criterion
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class ResolverException(Exception):
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"""A base class for all exceptions raised by this module.
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Exceptions derived by this class should all be handled in this module. Any
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bubbling pass the resolver should be treated as a bug.
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"""
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class RequirementsConflicted(ResolverException, Generic[RT, CT]):
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def __init__(self, criterion: Criterion[RT, CT]) -> None:
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super().__init__(criterion)
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self.criterion = criterion
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def __str__(self) -> str:
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return "Requirements conflict: {}".format(
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", ".join(repr(r) for r in self.criterion.iter_requirement()),
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)
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class InconsistentCandidate(ResolverException, Generic[RT, CT]):
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def __init__(self, candidate: CT, criterion: Criterion[RT, CT]):
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super().__init__(candidate, criterion)
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self.candidate = candidate
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self.criterion = criterion
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def __str__(self) -> str:
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return "Provided candidate {!r} does not satisfy {}".format(
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self.candidate,
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", ".join(repr(r) for r in self.criterion.iter_requirement()),
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)
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class ResolutionError(ResolverException):
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pass
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class ResolutionImpossible(ResolutionError, Generic[RT, CT]):
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def __init__(self, causes: Collection[RequirementInformation[RT, CT]]):
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super().__init__(causes)
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# causes is a list of RequirementInformation objects
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self.causes = causes
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class ResolutionTooDeep(ResolutionError):
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def __init__(self, round_count: int) -> None:
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super().__init__(round_count)
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self.round_count = round_count
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from __future__ import annotations
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import collections
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import itertools
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import operator
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from typing import TYPE_CHECKING, Generic
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from ..structs import (
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CT,
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KT,
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RT,
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DirectedGraph,
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IterableView,
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IteratorMapping,
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RequirementInformation,
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State,
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build_iter_view,
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)
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from .abstract import AbstractResolver, Result
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from .criterion import Criterion
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from .exceptions import (
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InconsistentCandidate,
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RequirementsConflicted,
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ResolutionImpossible,
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ResolutionTooDeep,
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ResolverException,
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)
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if TYPE_CHECKING:
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from collections.abc import Collection, Iterable, Mapping
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from ..providers import AbstractProvider, Preference
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from ..reporters import BaseReporter
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_OPTIMISTIC_BACKJUMPING_RATIO: float = 0.1
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def _build_result(state: State[RT, CT, KT]) -> Result[RT, CT, KT]:
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mapping = state.mapping
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all_keys: dict[int, KT | None] = {id(v): k for k, v in mapping.items()}
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all_keys[id(None)] = None
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graph: DirectedGraph[KT | None] = DirectedGraph()
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graph.add(None) # Sentinel as root dependencies' parent.
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connected: set[KT | None] = {None}
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for key, criterion in state.criteria.items():
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if not _has_route_to_root(state.criteria, key, all_keys, connected):
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continue
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if key not in graph:
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graph.add(key)
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for p in criterion.iter_parent():
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try:
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pkey = all_keys[id(p)]
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except KeyError:
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continue
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if pkey not in graph:
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graph.add(pkey)
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graph.connect(pkey, key)
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return Result(
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mapping={k: v for k, v in mapping.items() if k in connected},
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graph=graph,
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criteria=state.criteria,
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)
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class Resolution(Generic[RT, CT, KT]):
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"""Stateful resolution object.
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This is designed as a one-off object that holds information to kick start
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the resolution process, and holds the results afterwards.
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"""
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def __init__(
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self,
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provider: AbstractProvider[RT, CT, KT],
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reporter: BaseReporter[RT, CT, KT],
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) -> None:
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self._p = provider
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self._r = reporter
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self._states: list[State[RT, CT, KT]] = []
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# Optimistic backjumping variables
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self._optimistic_backjumping_ratio = _OPTIMISTIC_BACKJUMPING_RATIO
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self._save_states: list[State[RT, CT, KT]] | None = None
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self._optimistic_start_round: int | None = None
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@property
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def state(self) -> State[RT, CT, KT]:
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try:
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return self._states[-1]
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except IndexError as e:
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raise AttributeError("state") from e
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def _push_new_state(self) -> None:
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"""Push a new state into history.
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This new state will be used to hold resolution results of the next
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coming round.
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"""
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base = self._states[-1]
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state = State(
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mapping=base.mapping.copy(),
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criteria=base.criteria.copy(),
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backtrack_causes=base.backtrack_causes[:],
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)
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self._states.append(state)
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def _add_to_criteria(
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self,
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criteria: dict[KT, Criterion[RT, CT]],
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requirement: RT,
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parent: CT | None,
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) -> None:
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self._r.adding_requirement(requirement=requirement, parent=parent)
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identifier = self._p.identify(requirement_or_candidate=requirement)
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criterion = criteria.get(identifier)
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if criterion:
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incompatibilities = list(criterion.incompatibilities)
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else:
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incompatibilities = []
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matches = self._p.find_matches(
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identifier=identifier,
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requirements=IteratorMapping(
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criteria,
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operator.methodcaller("iter_requirement"),
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{identifier: [requirement]},
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),
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incompatibilities=IteratorMapping(
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criteria,
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operator.attrgetter("incompatibilities"),
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{identifier: incompatibilities},
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),
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)
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if criterion:
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information = list(criterion.information)
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information.append(RequirementInformation(requirement, parent))
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else:
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information = [RequirementInformation(requirement, parent)]
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criterion = Criterion(
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candidates=build_iter_view(matches),
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information=information,
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incompatibilities=incompatibilities,
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)
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if not criterion.candidates:
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raise RequirementsConflicted(criterion)
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criteria[identifier] = criterion
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def _remove_information_from_criteria(
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self, criteria: dict[KT, Criterion[RT, CT]], parents: Collection[KT]
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) -> None:
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"""Remove information from parents of criteria.
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Concretely, removes all values from each criterion's ``information``
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field that have one of ``parents`` as provider of the requirement.
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:param criteria: The criteria to update.
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:param parents: Identifiers for which to remove information from all criteria.
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"""
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if not parents:
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return
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for key, criterion in criteria.items():
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criteria[key] = Criterion(
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criterion.candidates,
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[
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information
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for information in criterion.information
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if (
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information.parent is None
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or self._p.identify(information.parent) not in parents
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)
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],
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criterion.incompatibilities,
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)
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def _get_preference(self, name: KT) -> Preference:
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return self._p.get_preference(
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identifier=name,
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resolutions=self.state.mapping,
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candidates=IteratorMapping(
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self.state.criteria,
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operator.attrgetter("candidates"),
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),
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information=IteratorMapping(
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self.state.criteria,
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operator.attrgetter("information"),
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),
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backtrack_causes=self.state.backtrack_causes,
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)
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def _is_current_pin_satisfying(
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self, name: KT, criterion: Criterion[RT, CT]
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) -> bool:
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try:
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current_pin = self.state.mapping[name]
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except KeyError:
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return False
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return all(
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self._p.is_satisfied_by(requirement=r, candidate=current_pin)
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for r in criterion.iter_requirement()
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)
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def _get_updated_criteria(self, candidate: CT) -> dict[KT, Criterion[RT, CT]]:
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criteria = self.state.criteria.copy()
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for requirement in self._p.get_dependencies(candidate=candidate):
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self._add_to_criteria(criteria, requirement, parent=candidate)
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return criteria
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def _attempt_to_pin_criterion(self, name: KT) -> list[Criterion[RT, CT]]:
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criterion = self.state.criteria[name]
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causes: list[Criterion[RT, CT]] = []
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for candidate in criterion.candidates:
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try:
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criteria = self._get_updated_criteria(candidate)
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except RequirementsConflicted as e:
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self._r.rejecting_candidate(e.criterion, candidate)
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causes.append(e.criterion)
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continue
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# Check the newly-pinned candidate actually works. This should
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# always pass under normal circumstances, but in the case of a
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# faulty provider, we will raise an error to notify the implementer
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# to fix find_matches() and/or is_satisfied_by().
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satisfied = all(
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self._p.is_satisfied_by(requirement=r, candidate=candidate)
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for r in criterion.iter_requirement()
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)
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if not satisfied:
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raise InconsistentCandidate(candidate, criterion)
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self._r.pinning(candidate=candidate)
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self.state.criteria.update(criteria)
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# Put newly-pinned candidate at the end. This is essential because
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# backtracking looks at this mapping to get the last pin.
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self.state.mapping.pop(name, None)
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self.state.mapping[name] = candidate
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return []
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# All candidates tried, nothing works. This criterion is a dead
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# end, signal for backtracking.
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return causes
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def _patch_criteria(
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self, incompatibilities_from_broken: list[tuple[KT, list[CT]]]
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) -> bool:
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# Create a new state from the last known-to-work one, and apply
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# the previously gathered incompatibility information.
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for k, incompatibilities in incompatibilities_from_broken:
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if not incompatibilities:
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continue
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try:
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criterion = self.state.criteria[k]
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except KeyError:
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continue
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matches = self._p.find_matches(
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identifier=k,
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requirements=IteratorMapping(
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self.state.criteria,
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operator.methodcaller("iter_requirement"),
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),
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incompatibilities=IteratorMapping(
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self.state.criteria,
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operator.attrgetter("incompatibilities"),
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{k: incompatibilities},
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),
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)
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candidates: IterableView[CT] = build_iter_view(matches)
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if not candidates:
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return False
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incompatibilities.extend(criterion.incompatibilities)
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self.state.criteria[k] = Criterion(
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candidates=candidates,
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information=list(criterion.information),
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incompatibilities=incompatibilities,
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)
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return True
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def _save_state(self) -> None:
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"""Save states for potential rollback if optimistic backjumping fails."""
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if self._save_states is None:
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self._save_states = [
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State(
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mapping=s.mapping.copy(),
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criteria=s.criteria.copy(),
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backtrack_causes=s.backtrack_causes[:],
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)
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for s in self._states
|
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]
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def _rollback_states(self) -> None:
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"""Rollback states and disable optimistic backjumping."""
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self._optimistic_backjumping_ratio = 0.0
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if self._save_states:
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self._states = self._save_states
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self._save_states = None
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||||
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def _backjump(self, causes: list[RequirementInformation[RT, CT]]) -> bool:
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"""Perform backjumping.
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||||
|
||||
When we enter here, the stack is like this::
|
||||
|
||||
[ state Z ]
|
||||
[ state Y ]
|
||||
[ state X ]
|
||||
.... earlier states are irrelevant.
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1. No pins worked for Z, so it does not have a pin.
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2. We want to reset state Y to unpinned, and pin another candidate.
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3. State X holds what state Y was before the pin, but does not
|
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have the incompatibility information gathered in state Y.
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Each iteration of the loop will:
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|
||||
1. Identify Z. The incompatibility is not always caused by the latest
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state. For example, given three requirements A, B and C, with
|
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dependencies A1, B1 and C1, where A1 and B1 are incompatible: the
|
||||
last state might be related to C, so we want to discard the
|
||||
previous state.
|
||||
2. Discard Z.
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||||
3. Discard Y but remember its incompatibility information gathered
|
||||
previously, and the failure we're dealing with right now.
|
||||
4. Push a new state Y' based on X, and apply the incompatibility
|
||||
information from Y to Y'.
|
||||
5a. If this causes Y' to conflict, we need to backtrack again. Make Y'
|
||||
the new Z and go back to step 2.
|
||||
5b. If the incompatibilities apply cleanly, end backtracking.
|
||||
"""
|
||||
incompatible_reqs: Iterable[CT | RT] = itertools.chain(
|
||||
(c.parent for c in causes if c.parent is not None),
|
||||
(c.requirement for c in causes),
|
||||
)
|
||||
incompatible_deps = {self._p.identify(r) for r in incompatible_reqs}
|
||||
while len(self._states) >= 3:
|
||||
# Remove the state that triggered backtracking.
|
||||
del self._states[-1]
|
||||
|
||||
# Optimistically backtrack to a state that caused the incompatibility
|
||||
broken_state = self.state
|
||||
while True:
|
||||
# Retrieve the last candidate pin and known incompatibilities.
|
||||
try:
|
||||
broken_state = self._states.pop()
|
||||
name, candidate = broken_state.mapping.popitem()
|
||||
except (IndexError, KeyError):
|
||||
raise ResolutionImpossible(causes) from None
|
||||
|
||||
if (
|
||||
not self._optimistic_backjumping_ratio
|
||||
and name not in incompatible_deps
|
||||
):
|
||||
# For safe backjumping only backjump if the current dependency
|
||||
# is not the same as the incompatible dependency
|
||||
break
|
||||
|
||||
# On the first time a non-safe backjump is done the state
|
||||
# is saved so we can restore it later if the resolution fails
|
||||
if (
|
||||
self._optimistic_backjumping_ratio
|
||||
and self._save_states is None
|
||||
and name not in incompatible_deps
|
||||
):
|
||||
self._save_state()
|
||||
|
||||
# If the current dependencies and the incompatible dependencies
|
||||
# are overlapping then we have likely found a cause of the
|
||||
# incompatibility
|
||||
current_dependencies = {
|
||||
self._p.identify(d) for d in self._p.get_dependencies(candidate)
|
||||
}
|
||||
if not current_dependencies.isdisjoint(incompatible_deps):
|
||||
break
|
||||
|
||||
# Fallback: We should not backtrack to the point where
|
||||
# broken_state.mapping is empty, so stop backtracking for
|
||||
# a chance for the resolution to recover
|
||||
if not broken_state.mapping:
|
||||
break
|
||||
|
||||
incompatibilities_from_broken = [
|
||||
(k, list(v.incompatibilities)) for k, v in broken_state.criteria.items()
|
||||
]
|
||||
|
||||
# Also mark the newly known incompatibility.
|
||||
incompatibilities_from_broken.append((name, [candidate]))
|
||||
|
||||
self._push_new_state()
|
||||
success = self._patch_criteria(incompatibilities_from_broken)
|
||||
|
||||
# It works! Let's work on this new state.
|
||||
if success:
|
||||
return True
|
||||
|
||||
# State does not work after applying known incompatibilities.
|
||||
# Try the still previous state.
|
||||
|
||||
# No way to backtrack anymore.
|
||||
return False
|
||||
|
||||
def _extract_causes(
|
||||
self, criteron: list[Criterion[RT, CT]]
|
||||
) -> list[RequirementInformation[RT, CT]]:
|
||||
"""Extract causes from list of criterion and deduplicate"""
|
||||
return list({id(i): i for c in criteron for i in c.information}.values())
|
||||
|
||||
def resolve(self, requirements: Iterable[RT], max_rounds: int) -> State[RT, CT, KT]:
|
||||
if self._states:
|
||||
raise RuntimeError("already resolved")
|
||||
|
||||
self._r.starting()
|
||||
|
||||
# Initialize the root state.
|
||||
self._states = [
|
||||
State(
|
||||
mapping=collections.OrderedDict(),
|
||||
criteria={},
|
||||
backtrack_causes=[],
|
||||
)
|
||||
]
|
||||
for r in requirements:
|
||||
try:
|
||||
self._add_to_criteria(self.state.criteria, r, parent=None)
|
||||
except RequirementsConflicted as e:
|
||||
raise ResolutionImpossible(e.criterion.information) from e
|
||||
|
||||
# The root state is saved as a sentinel so the first ever pin can have
|
||||
# something to backtrack to if it fails. The root state is basically
|
||||
# pinning the virtual "root" package in the graph.
|
||||
self._push_new_state()
|
||||
|
||||
# Variables for optimistic backjumping
|
||||
optimistic_rounds_cutoff: int | None = None
|
||||
optimistic_backjumping_start_round: int | None = None
|
||||
|
||||
for round_index in range(max_rounds):
|
||||
self._r.starting_round(index=round_index)
|
||||
|
||||
# Handle if optimistic backjumping has been running for too long
|
||||
if self._optimistic_backjumping_ratio and self._save_states is not None:
|
||||
if optimistic_backjumping_start_round is None:
|
||||
optimistic_backjumping_start_round = round_index
|
||||
optimistic_rounds_cutoff = int(
|
||||
(max_rounds - round_index) * self._optimistic_backjumping_ratio
|
||||
)
|
||||
|
||||
if optimistic_rounds_cutoff <= 0:
|
||||
self._rollback_states()
|
||||
continue
|
||||
elif optimistic_rounds_cutoff is not None:
|
||||
if (
|
||||
round_index - optimistic_backjumping_start_round
|
||||
>= optimistic_rounds_cutoff
|
||||
):
|
||||
self._rollback_states()
|
||||
continue
|
||||
|
||||
unsatisfied_names = [
|
||||
key
|
||||
for key, criterion in self.state.criteria.items()
|
||||
if not self._is_current_pin_satisfying(key, criterion)
|
||||
]
|
||||
|
||||
# All criteria are accounted for. Nothing more to pin, we are done!
|
||||
if not unsatisfied_names:
|
||||
self._r.ending(state=self.state)
|
||||
return self.state
|
||||
|
||||
# keep track of satisfied names to calculate diff after pinning
|
||||
satisfied_names = set(self.state.criteria.keys()) - set(unsatisfied_names)
|
||||
|
||||
if len(unsatisfied_names) > 1:
|
||||
narrowed_unstatisfied_names = list(
|
||||
self._p.narrow_requirement_selection(
|
||||
identifiers=unsatisfied_names,
|
||||
resolutions=self.state.mapping,
|
||||
candidates=IteratorMapping(
|
||||
self.state.criteria,
|
||||
operator.attrgetter("candidates"),
|
||||
),
|
||||
information=IteratorMapping(
|
||||
self.state.criteria,
|
||||
operator.attrgetter("information"),
|
||||
),
|
||||
backtrack_causes=self.state.backtrack_causes,
|
||||
)
|
||||
)
|
||||
else:
|
||||
narrowed_unstatisfied_names = unsatisfied_names
|
||||
|
||||
# If there are no unsatisfied names use unsatisfied names
|
||||
if not narrowed_unstatisfied_names:
|
||||
raise RuntimeError("narrow_requirement_selection returned 0 names")
|
||||
|
||||
# If there is only 1 unsatisfied name skip calling self._get_preference
|
||||
if len(narrowed_unstatisfied_names) > 1:
|
||||
# Choose the most preferred unpinned criterion to try.
|
||||
name = min(narrowed_unstatisfied_names, key=self._get_preference)
|
||||
else:
|
||||
name = narrowed_unstatisfied_names[0]
|
||||
|
||||
failure_criterion = self._attempt_to_pin_criterion(name)
|
||||
|
||||
if failure_criterion:
|
||||
causes = self._extract_causes(failure_criterion)
|
||||
# Backjump if pinning fails. The backjump process puts us in
|
||||
# an unpinned state, so we can work on it in the next round.
|
||||
self._r.resolving_conflicts(causes=causes)
|
||||
|
||||
try:
|
||||
success = self._backjump(causes)
|
||||
except ResolutionImpossible:
|
||||
if self._optimistic_backjumping_ratio and self._save_states:
|
||||
failed_optimistic_backjumping = True
|
||||
else:
|
||||
raise
|
||||
else:
|
||||
failed_optimistic_backjumping = bool(
|
||||
not success
|
||||
and self._optimistic_backjumping_ratio
|
||||
and self._save_states
|
||||
)
|
||||
|
||||
if failed_optimistic_backjumping and self._save_states:
|
||||
self._rollback_states()
|
||||
else:
|
||||
self.state.backtrack_causes[:] = causes
|
||||
|
||||
# Dead ends everywhere. Give up.
|
||||
if not success:
|
||||
raise ResolutionImpossible(self.state.backtrack_causes)
|
||||
else:
|
||||
# discard as information sources any invalidated names
|
||||
# (unsatisfied names that were previously satisfied)
|
||||
newly_unsatisfied_names = {
|
||||
key
|
||||
for key, criterion in self.state.criteria.items()
|
||||
if key in satisfied_names
|
||||
and not self._is_current_pin_satisfying(key, criterion)
|
||||
}
|
||||
self._remove_information_from_criteria(
|
||||
self.state.criteria, newly_unsatisfied_names
|
||||
)
|
||||
# Pinning was successful. Push a new state to do another pin.
|
||||
self._push_new_state()
|
||||
|
||||
self._r.ending_round(index=round_index, state=self.state)
|
||||
|
||||
raise ResolutionTooDeep(max_rounds)
|
||||
|
||||
|
||||
class Resolver(AbstractResolver[RT, CT, KT]):
|
||||
"""The thing that performs the actual resolution work."""
|
||||
|
||||
base_exception = ResolverException
|
||||
|
||||
def resolve( # type: ignore[override]
|
||||
self,
|
||||
requirements: Iterable[RT],
|
||||
max_rounds: int = 100,
|
||||
) -> Result[RT, CT, KT]:
|
||||
"""Take a collection of constraints, spit out the resolution result.
|
||||
|
||||
The return value is a representation to the final resolution result. It
|
||||
is a tuple subclass with three public members:
|
||||
|
||||
* `mapping`: A dict of resolved candidates. Each key is an identifier
|
||||
of a requirement (as returned by the provider's `identify` method),
|
||||
and the value is the resolved candidate.
|
||||
* `graph`: A `DirectedGraph` instance representing the dependency tree.
|
||||
The vertices are keys of `mapping`, and each edge represents *why*
|
||||
a particular package is included. A special vertex `None` is
|
||||
included to represent parents of user-supplied requirements.
|
||||
* `criteria`: A dict of "criteria" that hold detailed information on
|
||||
how edges in the graph are derived. Each key is an identifier of a
|
||||
requirement, and the value is a `Criterion` instance.
|
||||
|
||||
The following exceptions may be raised if a resolution cannot be found:
|
||||
|
||||
* `ResolutionImpossible`: A resolution cannot be found for the given
|
||||
combination of requirements. The `causes` attribute of the
|
||||
exception is a list of (requirement, parent), giving the
|
||||
requirements that could not be satisfied.
|
||||
* `ResolutionTooDeep`: The dependency tree is too deeply nested and
|
||||
the resolver gave up. This is usually caused by a circular
|
||||
dependency, but you can try to resolve this by increasing the
|
||||
`max_rounds` argument.
|
||||
"""
|
||||
resolution = Resolution(self.provider, self.reporter)
|
||||
state = resolution.resolve(requirements, max_rounds=max_rounds)
|
||||
return _build_result(state)
|
||||
|
||||
|
||||
def _has_route_to_root(
|
||||
criteria: Mapping[KT, Criterion[RT, CT]],
|
||||
key: KT | None,
|
||||
all_keys: dict[int, KT | None],
|
||||
connected: set[KT | None],
|
||||
) -> bool:
|
||||
if key in connected:
|
||||
return True
|
||||
if key not in criteria:
|
||||
return False
|
||||
assert key is not None
|
||||
for p in criteria[key].iter_parent():
|
||||
try:
|
||||
pkey = all_keys[id(p)]
|
||||
except KeyError:
|
||||
continue
|
||||
if pkey in connected:
|
||||
connected.add(key)
|
||||
return True
|
||||
if _has_route_to_root(criteria, pkey, all_keys, connected):
|
||||
connected.add(key)
|
||||
return True
|
||||
return False
|
||||
Loading…
Add table
Add a link
Reference in a new issue