import collections
import math
from typing import TYPE_CHECKING, Dict, Iterable, Iterator, Mapping, Sequence, Union
from pip._vendor.resolvelib.providers import AbstractProvider
from .base import Candidate, Constraint, Requirement
from .candidates import REQUIRES_PYTHON_IDENTIFIER
from .factory import Factory
if TYPE_CHECKING:
from pip._vendor.resolvelib.providers import Preference
from pip._vendor.resolvelib.resolvers import RequirementInformation
PreferenceInformation = RequirementInformation[Requirement, Candidate]
_ProviderBase = AbstractProvider[Requirement, Candidate, str]
else:
_ProviderBase = AbstractProvider
# Notes on the relationship between the provider, the factory, and the
# candidate and requirement classes.
#
# The provider is a direct implementation of the resolvelib class. Its role
# is to deliver the API that resolvelib expects.
#
# Rather than work with completely abstract "requirement" and "candidate"
# concepts as resolvelib does, pip has concrete classes implementing these two
# ideas. The API of Requirement and Candidate objects are defined in the base
# classes, but essentially map fairly directly to the equivalent provider
# methods. In particular, `find_matches` and `is_satisfied_by` are
# requirement methods, and `get_dependencies` is a candidate method.
#
# The factory is the interface to pip's internal mechanisms. It is stateless,
# and is created by the resolver and held as a property of the provider. It is
# responsible for creating Requirement and Candidate objects, and provides
# services to those objects (access to pip's finder and preparer).
class PipProvider(_ProviderBase):
"""Pip's provider implementation for resolvelib.
:params constraints: A mapping of constraints specified by the user. Keys
are canonicalized project names.
:params ignore_dependencies: Whether the user specified ``--no-deps``.
:params upgrade_strategy: The user-specified upgrade strategy.
:params user_requested: A set of canonicalized package names that the user
supplied for pip to install/upgrade.
"""
def __init__(
self,
factory: Factory,
constraints: Dict[str, Constraint],
ignore_dependencies: bool,
upgrade_strategy: str,
user_requested: Dict[str, int],
) -> None:
self._factory = factory
self._constraints = constraints
self._ignore_dependencies = ignore_dependencies
self._upgrade_strategy = upgrade_strategy
self._user_requested = user_requested
self._known_depths: Dict[str, float] = collections.defaultdict(lambda: math.inf)
def identify(self, requirement_or_candidate: Union[Requirement, Candidate]) -> str:
return requirement_or_candidate.name
def get_preference( # type: ignore
self,
identifier: str,
resolutions: Mapping[str, Candidate],
candidates: Mapping[str, Iterator[Candidate]],
information: Mapping[str, Iterable["PreferenceInformation"]],
backtrack_causes: Sequence["PreferenceInformation"],
) -> "Preference":
"""Produce a sort key for given requirement based on preference.
The lower the return value is, the more preferred this group of
arguments is.
Currently pip considers the followings in order:
* Prefer if any of the known requirements is "direct", e.g. points to an
explicit URL.
* If equal, prefer if any requirement is "pinned", i.e. contains
operator ``===`` or ``==``.
* If equal, calculate an approximate "depth" and resolve requirements
closer to the user-specified requirements first.
* Order user-specified requirements by the order they are specified.
* If equal, prefers "non-free" requirements, i.e. contains at least one
operator, such as ``>=`` or ``<``.
* If equal, order alphabetically for consistency (helps debuggability).
"""
lookups = (r.get_candidate_lookup() for r, _ in information[identifier])
candidate, ireqs = zip(*lookups)
operators = [
specifier.operator
for specifier_set in (ireq.specifier for ireq in ireqs if ireq)
for specifier in specifier_set
]
direct = candidate is not None
pinned = any(op[:2] == "==" for op in operators)
unfree = bool(operators)
try:
requested_order: Union[int, float] = self._user_requested[identifier]
except KeyError:
requested_order = math.inf
parent_depths = (
self._known_depths[parent.name] if parent is not None else 0.0
for _, parent in information[identifier]
)
inferred_depth = min(d for d in parent_depths) + 1.0
else:
inferred_depth = 1.0
self._known_depths[identifier] = inferred_depth
requested_order = self._user_requested.get(identifier, math.inf)
# Requires-Python has only one candidate and the check is basically
# free, so we always do it first to avoid needless work if it fails.
requires_python = identifier == REQUIRES_PYTHON_IDENTIFIER
# HACK: Setuptools have a very long and solid backward compatibility
# track record, and extremely few projects would request a narrow,
# non-recent version range of it since that would break a lot things.
# (Most projects specify it only to request for an installer feature,
# which does not work, but that's another topic.) Intentionally
# delaying Setuptools helps reduce branches the resolver has to check.
# This serves as a temporary fix for issues like "apache-airlfow[all]"
# while we work on "proper" branch pruning techniques.
delay_this = identifier == "setuptools"
# Prefer the causes of backtracking on the assumption that the problem
# resolving the dependency tree is related to the failures that caused
# the backtracking
backtrack_cause = self.is_backtrack_cause(identifier, backtrack_causes)
return (
not requires_python,
delay_this,
not direct,
not pinned,
not backtrack_cause,
inferred_depth,
requested_order,
not unfree,
identifier,
)
def _get_constraint(self, identifier: str) -> Constraint:
if identifier in self._constraints:
return self._constraints[identifier]
# HACK: Theoratically we should check whether this identifier is a valid
# "NAME[EXTRAS]" format, and parse out the name part with packaging or
# some regular expression. But since pip's resolver only spits out
# three kinds of identifiers: normalized PEP 503 names, normalized names
# plus extras, and Requires-Python, we can cheat a bit here.
name, open_bracket, _ = identifier.partition("[")
if open_bracket and name in self._constraints:
return self._constraints[name]
return Constraint.empty()
def find_matches(
self,
identifier: str,
requirements: Mapping[str, Iterator[Requirement]],
incompatibilities: Mapping[str, Iterator[Candidate]],
) -> Iterable[Candidate]:
def _eligible_for_upgrade(name: str) -> bool:
"""Are upgrades allowed for this project?
This checks the upgrade strategy, and whether the project was one
that the user specified in the command line, in order to decide
whether we should upgrade if there's a newer version available.
(Note that we don't need access to the `--upgrade` flag, because
an upgrade strategy of "to-satisfy-only" means that `--upgrade`
was not specified).
"""
if self._upgrade_strategy == "eager":
return True
elif self._upgrade_strategy == "only-if-needed":
return name in self._user_requested
return False
return self._factory.find_candidates(
identifier=identifier,
requirements=requirements,
constraint=self._get_constraint(identifier),
prefers_installed=(not _eligible_for_upgrade(identifier)),
incompatibilities=incompatibilities,
)
def is_satisfied_by(self, requirement: Requirement, candidate: Candidate) -> bool:
return requirement.is_satisfied_by(candidate)
def get_dependencies(self, candidate: Candidate) -> Sequence[Requirement]:
with_requires = not self._ignore_dependencies
return [r for r in candidate.iter_dependencies(with_requires) if r is not None]
@staticmethod
def is_backtrack_cause(
identifier: str, backtrack_causes: Sequence["PreferenceInformation"]
) -> bool:
for backtrack_cause in backtrack_causes:
if identifier == backtrack_cause.requirement.name:
return True
if backtrack_cause.parent and identifier == backtrack_cause.parent.name:
return True
return False
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