483 lines
17 KiB
Python
483 lines
17 KiB
Python
import collections
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import operator
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from .providers import AbstractResolver
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from .structs import DirectedGraph, IteratorMapping, build_iter_view
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RequirementInformation = collections.namedtuple(
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"RequirementInformation", ["requirement", "parent"]
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)
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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):
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def __init__(self, criterion):
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super(RequirementsConflicted, self).__init__(criterion)
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self.criterion = criterion
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def __str__(self):
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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):
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def __init__(self, candidate, criterion):
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super(InconsistentCandidate, self).__init__(candidate, criterion)
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self.candidate = candidate
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self.criterion = criterion
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def __str__(self):
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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 Criterion(object):
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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__(self, candidates, information, incompatibilities):
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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):
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requirements = ", ".join(
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"({!r}, via={!r})".format(req, parent)
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for req, parent in self.information
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)
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return "Criterion({})".format(requirements)
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def iter_requirement(self):
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return (i.requirement for i in self.information)
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def iter_parent(self):
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return (i.parent for i in self.information)
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class ResolutionError(ResolverException):
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pass
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class ResolutionImpossible(ResolutionError):
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def __init__(self, causes):
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super(ResolutionImpossible, self).__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):
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super(ResolutionTooDeep, self).__init__(round_count)
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self.round_count = round_count
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# Resolution state in a round.
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State = collections.namedtuple("State", "mapping criteria backtrack_causes")
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class Resolution(object):
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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__(self, provider, reporter):
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self._p = provider
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self._r = reporter
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self._states = []
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@property
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def state(self):
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try:
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return self._states[-1]
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except IndexError:
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raise AttributeError("state")
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def _push_new_state(self):
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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(self, criteria, requirement, parent):
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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 _get_preference(self, name):
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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(self, name, criterion):
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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):
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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):
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criterion = self.state.criteria[name]
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causes = []
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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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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 _backtrack(self):
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"""Perform backtracking.
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When we enter here, the stack is like this::
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[ state Z ]
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[ state Y ]
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[ state X ]
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.... 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. Discard Z.
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2. Discard Y but remember its incompatibility information gathered
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previously, and the failure we're dealing with right now.
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3. Push a new state Y' based on X, and apply the incompatibility
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information from Y to Y'.
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4a. If this causes Y' to conflict, we need to backtrack again. Make Y'
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the new Z and go back to step 2.
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4b. If the incompatibilities apply cleanly, end backtracking.
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"""
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while len(self._states) >= 3:
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# Remove the state that triggered backtracking.
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del self._states[-1]
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# Retrieve the last candidate pin and known incompatibilities.
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broken_state = self._states.pop()
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name, candidate = broken_state.mapping.popitem()
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incompatibilities_from_broken = [
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(k, list(v.incompatibilities))
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for k, v in broken_state.criteria.items()
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]
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# Also mark the newly known incompatibility.
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incompatibilities_from_broken.append((name, [candidate]))
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self._r.backtracking(candidate=candidate)
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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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def _patch_criteria():
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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 = 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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self._push_new_state()
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success = _patch_criteria()
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# It works! Let's work on this new state.
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if success:
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return True
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# State does not work after applying known incompatibilities.
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# Try the still previous state.
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# No way to backtrack anymore.
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return False
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def resolve(self, requirements, max_rounds):
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if self._states:
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raise RuntimeError("already resolved")
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self._r.starting()
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# Initialize the root state.
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self._states = [
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State(
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mapping=collections.OrderedDict(),
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criteria={},
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backtrack_causes=[],
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)
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]
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for r in requirements:
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try:
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self._add_to_criteria(self.state.criteria, r, parent=None)
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except RequirementsConflicted as e:
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raise ResolutionImpossible(e.criterion.information)
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# The root state is saved as a sentinel so the first ever pin can have
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# something to backtrack to if it fails. The root state is basically
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# pinning the virtual "root" package in the graph.
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self._push_new_state()
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for round_index in range(max_rounds):
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self._r.starting_round(index=round_index)
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unsatisfied_names = [
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key
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for key, criterion in self.state.criteria.items()
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if not self._is_current_pin_satisfying(key, criterion)
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]
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# All criteria are accounted for. Nothing more to pin, we are done!
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if not unsatisfied_names:
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self._r.ending(state=self.state)
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return self.state
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# Choose the most preferred unpinned criterion to try.
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name = min(unsatisfied_names, key=self._get_preference)
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failure_causes = self._attempt_to_pin_criterion(name)
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if failure_causes:
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causes = [i for c in failure_causes for i in c.information]
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# Backtrack if pinning fails. The backtrack process puts us in
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# an unpinned state, so we can work on it in the next round.
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self._r.resolving_conflicts(causes=causes)
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success = self._backtrack()
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self.state.backtrack_causes[:] = causes
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# Dead ends everywhere. Give up.
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if not success:
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raise ResolutionImpossible(self.state.backtrack_causes)
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else:
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# Pinning was successful. Push a new state to do another pin.
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self._push_new_state()
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self._r.ending_round(index=round_index, state=self.state)
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raise ResolutionTooDeep(max_rounds)
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def _has_route_to_root(criteria, key, all_keys, connected):
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if key in connected:
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return True
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if key not in criteria:
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return False
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for p in criteria[key].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 in connected:
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connected.add(key)
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return True
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if _has_route_to_root(criteria, pkey, all_keys, connected):
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connected.add(key)
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return True
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return False
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Result = collections.namedtuple("Result", "mapping graph criteria")
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def _build_result(state):
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mapping = state.mapping
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all_keys = {id(v): k for k, v in mapping.items()}
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all_keys[id(None)] = None
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graph = DirectedGraph()
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graph.add(None) # Sentinel as root dependencies' parent.
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connected = {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 Resolver(AbstractResolver):
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"""The thing that performs the actual resolution work."""
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base_exception = ResolverException
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def resolve(self, requirements, max_rounds=100):
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"""Take a collection of constraints, spit out the resolution result.
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The return value is a representation to the final resolution result. It
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is a tuple subclass with three public members:
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* `mapping`: A dict of resolved candidates. Each key is an identifier
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of a requirement (as returned by the provider's `identify` method),
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and the value is the resolved candidate.
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* `graph`: A `DirectedGraph` instance representing the dependency tree.
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The vertices are keys of `mapping`, and each edge represents *why*
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a particular package is included. A special vertex `None` is
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included to represent parents of user-supplied requirements.
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* `criteria`: A dict of "criteria" that hold detailed information on
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how edges in the graph are derived. Each key is an identifier of a
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requirement, and the value is a `Criterion` instance.
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The following exceptions may be raised if a resolution cannot be found:
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* `ResolutionImpossible`: A resolution cannot be found for the given
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combination of requirements. The `causes` attribute of the
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exception is a list of (requirement, parent), giving the
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requirements that could not be satisfied.
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* `ResolutionTooDeep`: The dependency tree is too deeply nested and
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the resolver gave up. This is usually caused by a circular
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dependency, but you can try to resolve this by increasing the
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`max_rounds` argument.
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"""
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resolution = Resolution(self.provider, self.reporter)
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state = resolution.resolve(requirements, max_rounds=max_rounds)
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return _build_result(state)
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