AndroidRemoteController/python/backup/site-packages/pip/_vendor/resolvelib/resolvers/resolution.py

from __future__ import annotations

import collections
import itertools
import operator
from typing import TYPE_CHECKING, Generic

from ..structs import (
    CT,
    KT,
    RT,
    DirectedGraph,
    IterableView,
    IteratorMapping,
    RequirementInformation,
    State,
    build_iter_view,
)
from .abstract import AbstractResolver, Result
from .criterion import Criterion
from .exceptions import (
    InconsistentCandidate,
    RequirementsConflicted,
    ResolutionImpossible,
    ResolutionTooDeep,
    ResolverException,
)

if TYPE_CHECKING:
    from collections.abc import Collection, Iterable, Mapping

    from ..providers import AbstractProvider, Preference
    from ..reporters import BaseReporter

_OPTIMISTIC_BACKJUMPING_RATIO: float = 0.1


def _build_result(state: State[RT, CT, KT]) -> Result[RT, CT, KT]:
    mapping = state.mapping
    all_keys: dict[int, KT | None] = {id(v): k for k, v in mapping.items()}
    all_keys[id(None)] = None

    graph: DirectedGraph[KT | None] = DirectedGraph()
    graph.add(None)  # Sentinel as root dependencies' parent.

    connected: set[KT | None] = {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 Resolution(Generic[RT, CT, KT]):
    """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: AbstractProvider[RT, CT, KT],
        reporter: BaseReporter[RT, CT, KT],
    ) -> None:
        self._p = provider
        self._r = reporter
        self._states: list[State[RT, CT, KT]] = []

        # Optimistic backjumping variables
        self._optimistic_backjumping_ratio = _OPTIMISTIC_BACKJUMPING_RATIO
        self._save_states: list[State[RT, CT, KT]] | None = None
        self._optimistic_start_round: int | None = None

    @property
    def state(self) -> State[RT, CT, KT]:
        try:
            return self._states[-1]
        except IndexError as e:
            raise AttributeError("state") from e

    def _push_new_state(self) -> None:
        """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: dict[KT, Criterion[RT, CT]],
        requirement: RT,
        parent: CT | None,
    ) -> None:
        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 _remove_information_from_criteria(
        self, criteria: dict[KT, Criterion[RT, CT]], parents: Collection[KT]
    ) -> None:
        """Remove information from parents of criteria.

        Concretely, removes all values from each criterion's ``information``
        field that have one of ``parents`` as provider of the requirement.

        :param criteria: The criteria to update.
        :param parents: Identifiers for which to remove information from all criteria.
        """
        if not parents:
            return
        for key, criterion in criteria.items():
            criteria[key] = Criterion(
                criterion.candidates,
                [
                    information
                    for information in criterion.information
                    if (
                        information.parent is None
                        or self._p.identify(information.parent) not in parents
                    )
                ],
                criterion.incompatibilities,
            )

    def _get_preference(self, name: KT) -> Preference:
        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: KT, criterion: Criterion[RT, CT]
    ) -> bool:
        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: CT) -> dict[KT, Criterion[RT, CT]]:
        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: KT) -> list[Criterion[RT, CT]]:
        criterion = self.state.criteria[name]

        causes: list[Criterion[RT, CT]] = []
        for candidate in criterion.candidates:
            try:
                criteria = self._get_updated_criteria(candidate)
            except RequirementsConflicted as e:
                self._r.rejecting_candidate(e.criterion, candidate)
                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 _patch_criteria(
        self, incompatibilities_from_broken: list[tuple[KT, list[CT]]]
    ) -> bool:
        # Create a new state from the last known-to-work one, and apply
        # the previously gathered incompatibility information.
        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: IterableView[CT] = 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

    def _save_state(self) -> None:
        """Save states for potential rollback if optimistic backjumping fails."""
        if self._save_states is None:
            self._save_states = [
                State(
                    mapping=s.mapping.copy(),
                    criteria=s.criteria.copy(),
                    backtrack_causes=s.backtrack_causes[:],
                )
                for s in self._states
            ]

    def _rollback_states(self) -> None:
        """Rollback states and disable optimistic backjumping."""
        self._optimistic_backjumping_ratio = 0.0
        if self._save_states:
            self._states = self._save_states
            self._save_states = None

    def _backjump(self, causes: list[RequirementInformation[RT, CT]]) -> bool:
        """Perform backjumping.

        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.  Identify Z. The incompatibility is not always caused by the latest
            state. For example, given three requirements A, B and C, with
            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.
        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

                # Guard: We need at least two state to remain to both
                # backtrack and push a new state
                if len(self._states) <= 1:
                    raise ResolutionImpossible(causes)

            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