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import collections
import operator
from .providers import AbstractResolver
from .structs import DirectedGraph, IteratorMapping, build_iter_view
RequirementInformation = collections.namedtuple(
"RequirementInformation", ["requirement", "parent"]
)
class ResolverException(Exception):
"""A base class for all exceptions raised by this module.
Exceptions derived by this class should all be handled in this module. Any
bubbling pass the resolver should be treated as a bug.
"""
class RequirementsConflicted(ResolverException):
def __init__(self, criterion):
super(RequirementsConflicted, self).__init__(criterion)
self.criterion = criterion
def __str__(self):
return "Requirements conflict: {}".format(
", ".join(repr(r) for r in self.criterion.iter_requirement()),
)
class InconsistentCandidate(ResolverException):
def __init__(self, candidate, criterion):
super(InconsistentCandidate, self).__init__(candidate, criterion)
self.candidate = candidate
self.criterion = criterion
def __str__(self):
return "Provided candidate {!r} does not satisfy {}".format(
self.candidate,
", ".join(repr(r) for r in self.criterion.iter_requirement()),
)
class Criterion(object):
"""Representation of possible resolution results of a package.
This holds three attributes:
* `information` is a collection of `RequirementInformation` pairs.
Each pair is a requirement contributing to this criterion, and the
candidate that provides the requirement.
* `incompatibilities` is a collection of all known not-to-work candidates
to exclude from consideration.
* `candidates` is a collection containing all possible candidates deducted
from the union of contributing requirements and known incompatibilities.
It should never be empty, except when the criterion is an attribute of a
raised `RequirementsConflicted` (in which case it is always empty).
.. note::
This class is intended to be externally immutable. **Do not** mutate
any of its attribute containers.
"""
def __init__(self, candidates, information, incompatibilities):
self.candidates = candidates
self.information = information
self.incompatibilities = incompatibilities
def __repr__(self):
requirements = ", ".join(
"({!r}, via={!r})".format(req, parent)
for req, parent in self.information
)
return "Criterion({})".format(requirements)
def iter_requirement(self):
return (i.requirement for i in self.information)
def iter_parent(self):
return (i.parent for i in self.information)
class ResolutionError(ResolverException):
pass
class ResolutionImpossible(ResolutionError):
def __init__(self, causes):
super(ResolutionImpossible, self).__init__(causes)
# causes is a list of RequirementInformation objects
self.causes = causes
class ResolutionTooDeep(ResolutionError):
def __init__(self, round_count):
super(ResolutionTooDeep, self).__init__(round_count)
self.round_count = round_count
# Resolution state in a round.
State = collections.namedtuple("State", "mapping criteria backtrack_causes")
class Resolution(object):
"""Stateful resolution object.
This is designed as a one-off object that holds information to kick start
the resolution process, and holds the results afterwards.
"""
def __init__(self, provider, reporter):
self._p = provider
self._r = reporter
self._states = []
@property
def state(self):
try:
return self._states[-1]
except IndexError:
raise AttributeError("state")
def _push_new_state(self):
"""Push a new state into history.
This new state will be used to hold resolution results of the next
coming round.
"""
base = self._states[-1]
state = State(
mapping=base.mapping.copy(),
criteria=base.criteria.copy(),
backtrack_causes=base.backtrack_causes[:],
)
self._states.append(state)
def _add_to_criteria(self, criteria, requirement, parent):
self._r.adding_requirement(requirement=requirement, parent=parent)
identifier = self._p.identify(requirement_or_candidate=requirement)
criterion = criteria.get(identifier)
if criterion:
incompatibilities = list(criterion.incompatibilities)
else:
incompatibilities = []
matches = self._p.find_matches(
identifier=identifier,
requirements=IteratorMapping(
criteria,
operator.methodcaller("iter_requirement"),
{identifier: [requirement]},
),
incompatibilities=IteratorMapping(
criteria,
operator.attrgetter("incompatibilities"),
{identifier: incompatibilities},
),
)
if criterion:
information = list(criterion.information)
information.append(RequirementInformation(requirement, parent))
else:
information = [RequirementInformation(requirement, parent)]
criterion = Criterion(
candidates=build_iter_view(matches),
information=information,
incompatibilities=incompatibilities,
)
if not criterion.candidates:
raise RequirementsConflicted(criterion)
criteria[identifier] = criterion
def _get_preference(self, name):
return self._p.get_preference(
identifier=name,
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,
)
def _is_current_pin_satisfying(self, name, criterion):
try:
current_pin = self.state.mapping[name]
except KeyError:
return False
return all(
self._p.is_satisfied_by(requirement=r, candidate=current_pin)
for r in criterion.iter_requirement()
)
def _get_updated_criteria(self, candidate):
criteria = self.state.criteria.copy()
for requirement in self._p.get_dependencies(candidate=candidate):
self._add_to_criteria(criteria, requirement, parent=candidate)
return criteria
def _attempt_to_pin_criterion(self, name):
criterion = self.state.criteria[name]
causes = []
for candidate in criterion.candidates:
try:
criteria = self._get_updated_criteria(candidate)
except RequirementsConflicted as e:
causes.append(e.criterion)
continue
# Check the newly-pinned candidate actually works. This should
# always pass under normal circumstances, but in the case of a
# faulty provider, we will raise an error to notify the implementer
# to fix find_matches() and/or is_satisfied_by().
satisfied = all(
self._p.is_satisfied_by(requirement=r, candidate=candidate)
for r in criterion.iter_requirement()
)
if not satisfied:
raise InconsistentCandidate(candidate, criterion)
self._r.pinning(candidate=candidate)
self.state.criteria.update(criteria)
# Put newly-pinned candidate at the end. This is essential because
# backtracking looks at this mapping to get the last pin.
self.state.mapping.pop(name, None)
self.state.mapping[name] = candidate
return []
# All candidates tried, nothing works. This criterion is a dead
# end, signal for backtracking.
return causes
def _backtrack(self):
"""Perform backtracking.
When we enter here, the stack is like this::
[ state Z ]
[ state Y ]
[ state X ]
.... earlier states are irrelevant.
1. No pins worked for Z, so it does not have a pin.
2. We want to reset state Y to unpinned, and pin another candidate.
3. State X holds what state Y was before the pin, but does not
have the incompatibility information gathered in state Y.
Each iteration of the loop will:
1. Discard Z.
2. Discard Y but remember its incompatibility information gathered
previously, and the failure we're dealing with right now.
3. Push a new state Y' based on X, and apply the incompatibility
information from Y to Y'.
4a. If this causes Y' to conflict, we need to backtrack again. Make Y'
the new Z and go back to step 2.
4b. If the incompatibilities apply cleanly, end backtracking.
"""
while len(self._states) >= 3:
# Remove the state that triggered backtracking.
del self._states[-1]
# Retrieve the last candidate pin and known incompatibilities.
broken_state = self._states.pop()
name, candidate = broken_state.mapping.popitem()
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._r.backtracking(candidate=candidate)
# Create a new state from the last known-to-work one, and apply
# the previously gathered incompatibility information.
def _patch_criteria():
for k, incompatibilities in incompatibilities_from_broken:
if not incompatibilities:
continue
try:
criterion = self.state.criteria[k]
except KeyError:
continue
matches = self._p.find_matches(
identifier=k,
requirements=IteratorMapping(
self.state.criteria,
operator.methodcaller("iter_requirement"),
),
incompatibilities=IteratorMapping(
self.state.criteria,
operator.attrgetter("incompatibilities"),
{k: incompatibilities},
),
)
candidates = build_iter_view(matches)
if not candidates:
return False
incompatibilities.extend(criterion.incompatibilities)
self.state.criteria[k] = Criterion(
candidates=candidates,
information=list(criterion.information),
incompatibilities=incompatibilities,
)
return True
self._push_new_state()
success = _patch_criteria()
# 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 resolve(self, requirements, max_rounds):
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)
# 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()
for round_index in range(max_rounds):
self._r.starting_round(index=round_index)
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
# Choose the most preferred unpinned criterion to try.
name = min(unsatisfied_names, key=self._get_preference)
failure_causes = self._attempt_to_pin_criterion(name)
if failure_causes:
# Backtrack if pinning fails. The backtrack process puts us in
# an unpinned state, so we can work on it in the next round.
success = self._backtrack()
self.state.backtrack_causes[:] = [
i for c in failure_causes for i in c.information
]
# Dead ends everywhere. Give up.
if not success:
raise ResolutionImpossible(self.state.backtrack_causes)
else:
# 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)
def _has_route_to_root(criteria, key, all_keys, connected):
if key in connected:
return True
if key not in criteria:
return False
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
Result = collections.namedtuple("Result", "mapping graph criteria")
def _build_result(state):
mapping = state.mapping
all_keys = {id(v): k for k, v in mapping.items()}
all_keys[id(None)] = None
graph = DirectedGraph()
graph.add(None) # Sentinel as root dependencies' parent.
connected = {None}
for key, criterion in state.criteria.items():
if not _has_route_to_root(state.criteria, key, all_keys, connected):
continue
if key not in graph:
graph.add(key)
for p in criterion.iter_parent():
try:
pkey = all_keys[id(p)]
except KeyError:
continue
if pkey not in graph:
graph.add(pkey)
graph.connect(pkey, key)
return Result(
mapping={k: v for k, v in mapping.items() if k in connected},
graph=graph,
criteria=state.criteria,
)
class Resolver(AbstractResolver):
"""The thing that performs the actual resolution work."""
base_exception = ResolverException
def resolve(self, requirements, max_rounds=100):
"""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)
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