"""Top-level logic for the semantic analyzer.

The semantic analyzer binds names, resolves imports, detects various

special constructs that don't have dedicated AST nodes after parse

(such as 'cast' which looks like a call), populates symbol tables, and

performs various simple consistency checks.

Semantic analysis of each SCC (strongly connected component; import

cycle) is performed in one unit. Each module is analyzed as multiple

separate *targets*; the module top level is one target and each function

is a target. Nested functions are not separate targets, however. This is

mostly identical to targets used by mypy daemon (but classes aren't

targets in semantic analysis).

We first analyze each module top level in an SCC. If we encounter some

names that we can't bind because the target of the name may not have

been processed yet, we *defer* the current target for further

processing. Deferred targets will be analyzed additional times until

everything can be bound, or we reach a maximum number of iterations.

We keep track of a set of incomplete namespaces, i.e. namespaces that we

haven't finished populating yet. References to these namespaces cause a

deferral if they can't be satisfied. Initially every module in the SCC

will be incomplete.

"""

from __future__ import annotations

from contextlib import nullcontext

from typing import TYPE_CHECKING, Callable, Final, List, Optional, Tuple, Union

from typing_extensions import TypeAlias as _TypeAlias

import mypy.build

import mypy.state

from mypy.checker import FineGrainedDeferredNode

from mypy.errors import Errors

from mypy.nodes import Decorator, FuncDef, MypyFile, OverloadedFuncDef, TypeInfo, Var

from mypy.options import Options

from mypy.plugin import ClassDefContext

from mypy.plugins import dataclasses as dataclasses_plugin

from mypy.semanal import (

SemanticAnalyzer,

apply_semantic_analyzer_patches,

remove_imported_names_from_symtable,

)

from mypy.semanal_classprop import (

add_type_promotion,

calculate_class_abstract_status,

calculate_class_vars,

check_protocol_status,

)

from mypy.semanal_infer import infer_decorator_signature_if_simple

from mypy.semanal_shared import find_dataclass_transform_spec

from mypy.semanal_typeargs import TypeArgumentAnalyzer

from mypy.server.aststrip import SavedAttributes

from mypy.util import is_typeshed_file

if TYPE_CHECKING:

from mypy.build import Graph, State

Patches: _TypeAlias = List[Tuple[int, Callable[[], None]]]

# If we perform this many iterations, raise an exception since we are likely stuck.

MAX_ITERATIONS: Final = 20

# Number of passes over core modules before going on to the rest of the builtin SCC.

CORE_WARMUP: Final = 2

core_modules: Final = [

"typing",

"_collections_abc",

"builtins",

"abc",

"collections",

"collections.abc",

]

def semantic_analysis_for_scc(graph: Graph, scc: list[str], errors: Errors) -> None:

"""Perform semantic analysis for all modules in a SCC (import cycle).

Assume that reachability analysis has already been performed.

The scc will be processed roughly in the order the modules are included

in the list.

"""

patches: Patches = []

# Note that functions can't define new module-level attributes

# using 'global x', since module top levels are fully processed

# before functions. This limitation is unlikely to go away soon.

process_top_levels(graph, scc, patches)

process_functions(graph, scc, patches)

# We use patch callbacks to fix up things when we expect relatively few

# callbacks to be required.

apply_semantic_analyzer_patches(patches)

# Run class decorator hooks (they requite complete MROs and no placeholders).

apply_class_plugin_hooks(graph, scc, errors)

# This pass might need fallbacks calculated above and the results of hooks.

check_type_arguments(graph, scc, errors)

calculate_class_properties(graph, scc, errors)

check_blockers(graph, scc)

# Clean-up builtins, so that TypeVar etc. are not accessible without importing.

if "builtins" in scc:

cleanup_builtin_scc(graph["builtins"])

def cleanup_builtin_scc(state: State) -> None:

"""Remove imported names from builtins namespace.

This way names imported from typing in builtins.pyi aren't available

by default (without importing them). We can only do this after processing

the whole SCC is finished, when the imported names aren't needed for

processing builtins.pyi itself.

"""

assert state.tree is not None

remove_imported_names_from_symtable(state.tree.names, "builtins")

def semantic_analysis_for_targets(

state: State, nodes: list[FineGrainedDeferredNode], graph: Graph, saved_attrs: SavedAttributes

) -> None:

"""Semantically analyze only selected nodes in a given module.

This essentially mirrors the logic of semantic_analysis_for_scc()

except that we process only some targets. This is used in fine grained

incremental mode, when propagating an update.

The saved_attrs are implicitly declared instance attributes (attributes

defined on self) removed by AST stripper that may need to be reintroduced

here. They must be added before any methods are analyzed.

"""

patches: Patches = []

if any(isinstance(n.node, MypyFile) for n in nodes):

# Process module top level first (if needed).

process_top_levels(graph, [state.id], patches)

restore_saved_attrs(saved_attrs)

analyzer = state.manager.semantic_analyzer

for n in nodes:

if isinstance(n.node, MypyFile):

# Already done above.

continue

process_top_level_function(

analyzer, state, state.id, n.node.fullname, n.node, n.active_typeinfo, patches

)

apply_semantic_analyzer_patches(patches)

apply_class_plugin_hooks(graph, [state.id], state.manager.errors)

check_type_arguments_in_targets(nodes, state, state.manager.errors)

calculate_class_properties(graph, [state.id], state.manager.errors)

def restore_saved_attrs(saved_attrs: SavedAttributes) -> None:

"""Restore instance variables removed during AST strip that haven't been added yet."""

for (cdef, name), sym in saved_attrs.items():

info = cdef.info

existing = info.get(name)

defined_in_this_class = name in info.names

assert isinstance(sym.node, Var)

# This needs to mimic the logic in SemanticAnalyzer.analyze_member_lvalue()

# regarding the existing variable in class body or in a superclass:

# If the attribute of self is not defined in superclasses, create a new Var.

if (

existing is None

or

# (An abstract Var is considered as not defined.)

(isinstance(existing.node, Var) and existing.node.is_abstract_var)

or

# Also an explicit declaration on self creates a new Var unless

# there is already one defined in the class body.

sym.node.explicit_self_type

and not defined_in_this_class

):

info.names[name] = sym

def process_top_levels(graph: Graph, scc: list[str], patches: Patches) -> None:

# Process top levels until everything has been bound.

# Reverse order of the scc so the first modules in the original list will be

# be processed first. This helps with performance.

scc = list(reversed(scc))

# Initialize ASTs and symbol tables.

for id in scc:

state = graph[id]

assert state.tree is not None

state.manager.semantic_analyzer.prepare_file(state.tree)

# Initially all namespaces in the SCC are incomplete (well they are empty).

state.manager.incomplete_namespaces.update(scc)

worklist = scc.copy()

# HACK: process core stuff first. This is mostly needed to support defining

# named tuples in builtin SCC.

if all(m in worklist for m in core_modules):

worklist += list(reversed(core_modules)) * CORE_WARMUP

final_iteration = False

iteration = 0

analyzer = state.manager.semantic_analyzer

analyzer.deferral_debug_context.clear()

while worklist:

iteration += 1

if iteration > MAX_ITERATIONS:

# Just pick some module inside the current SCC for error context.

assert state.tree is not None

with analyzer.file_context(state.tree, state.options):

analyzer.report_hang()

break

if final_iteration:

# Give up. It's impossible to bind all names.

state.manager.incomplete_namespaces.clear()

all_deferred: list[str] = []

any_progress = False

while worklist:

next_id = worklist.pop()

state = graph[next_id]

assert state.tree is not None

deferred, incomplete, progress = semantic_analyze_target(

next_id, next_id, state, state.tree, None, final_iteration, patches

)

all_deferred += deferred

any_progress = any_progress or progress

if not incomplete:

state.manager.incomplete_namespaces.discard(next_id)

if final_iteration:

assert not all_deferred, "Must not defer during final iteration"

# Reverse to process the targets in the same order on every iteration. This avoids

# processing the same target twice in a row, which is inefficient.

worklist = list(reversed(all_deferred))

final_iteration = not any_progress

def process_functions(graph: Graph, scc: list[str], patches: Patches) -> None:

# Process functions.

for module in scc:

tree = graph[module].tree

assert tree is not None

analyzer = graph[module].manager.semantic_analyzer

# In principle, functions can be processed in arbitrary order,

# but _methods_ must be processed in the order they are defined,

# because some features (most notably partial types) depend on

# order of definitions on self.

#

# There can be multiple generated methods per line. Use target

# name as the second sort key to get a repeatable sort order on

# Python 3.5, which doesn't preserve dictionary order.

targets = sorted(get_all_leaf_targets(tree), key=lambda x: (x[1].line, x[0]))

for target, node, active_type in targets:

assert isinstance(node, (FuncDef, OverloadedFuncDef, Decorator))

process_top_level_function(

analyzer, graph[module], module, target, node, active_type, patches

)

def process_top_level_function(

analyzer: SemanticAnalyzer,

state: State,

module: str,

target: str,

node: FuncDef | OverloadedFuncDef | Decorator,

active_type: TypeInfo | None,

patches: Patches,

) -> None:

"""Analyze single top-level function or method.

Process the body of the function (including nested functions) again and again,

until all names have been resolved (or iteration limit reached).

"""

# We need one more iteration after incomplete is False (e.g. to report errors, if any).

final_iteration = False

incomplete = True

# Start in the incomplete state (no missing names will be reported on first pass).

# Note that we use module name, since functions don't create qualified names.

deferred = [module]

analyzer.deferral_debug_context.clear()

analyzer.incomplete_namespaces.add(module)

iteration = 0

while deferred:

iteration += 1

if iteration == MAX_ITERATIONS:

# Just pick some module inside the current SCC for error context.

assert state.tree is not None

with analyzer.file_context(state.tree, state.options):

analyzer.report_hang()

break

if not (deferred or incomplete) or final_iteration:

# OK, this is one last pass, now missing names will be reported.

analyzer.incomplete_namespaces.discard(module)

deferred, incomplete, progress = semantic_analyze_target(

target, module, state, node, active_type, final_iteration, patches

)

if final_iteration:

assert not deferred, "Must not defer during final iteration"

if not progress:

final_iteration = True

analyzer.incomplete_namespaces.discard(module)

# After semantic analysis is done, discard local namespaces

# to avoid memory hoarding.

analyzer.saved_locals.clear()

TargetInfo: _TypeAlias = Tuple[

str, Union[MypyFile, FuncDef, OverloadedFuncDef, Decorator], Optional[TypeInfo]

]

def get_all_leaf_targets(file: MypyFile) -> list[TargetInfo]:

"""Return all leaf targets in a symbol table (module-level and methods)."""

result: list[TargetInfo] = []

for fullname, node, active_type in file.local_definitions():

if isinstance(node.node, (FuncDef, OverloadedFuncDef, Decorator)):

result.append((fullname, node.node, active_type))

return result

def semantic_analyze_target(

target: str,

module: str,

state: State,

node: MypyFile | FuncDef | OverloadedFuncDef | Decorator,

active_type: TypeInfo | None,

final_iteration: bool,

patches: Patches,

) -> tuple[list[str], bool, bool]:

"""Semantically analyze a single target.

Return tuple with these items:

- list of deferred targets

- was some definition incomplete (need to run another pass)

- were any new names defined (or placeholders replaced)

"""

state.manager.processed_targets.append((module, target))

tree = state.tree

assert tree is not None

analyzer = state.manager.semantic_analyzer

# TODO: Move initialization to somewhere else

analyzer.global_decls = [set()]

analyzer.nonlocal_decls = [set()]

analyzer.globals = tree.names

analyzer.progress = False

with state.wrap_context(check_blockers=False):

refresh_node = node

if isinstance(refresh_node, Decorator):

# Decorator expressions will be processed as part of the module top level.

refresh_node = refresh_node.func

analyzer.refresh_partial(

refresh_node,

patches,

final_iteration,

file_node=tree,

options=state.options,

active_type=active_type,

)

if isinstance(node, Decorator):

infer_decorator_signature_if_simple(node, analyzer)

for dep in analyzer.imports:

state.add_dependency(dep)

priority = mypy.build.PRI_LOW

if priority <= state.priorities.get(dep, priority):

state.priorities[dep] = priority

# Clear out some stale data to avoid memory leaks and astmerge

# validity check confusion

analyzer.statement = None

del analyzer.cur_mod_node

if analyzer.deferred:

return [target], analyzer.incomplete, analyzer.progress

else:

return [], analyzer.incomplete, analyzer.progress

def check_type_arguments(graph: Graph, scc: list[str], errors: Errors) -> None:

for module in scc:

state = graph[module]

assert state.tree

analyzer = TypeArgumentAnalyzer(

errors,

state.options,

state.tree.is_typeshed_file(state.options),

state.manager.semantic_analyzer.named_type,

)

with state.wrap_context():

with mypy.state.state.strict_optional_set(state.options.strict_optional):

state.tree.accept(analyzer)

def check_type_arguments_in_targets(

targets: list[FineGrainedDeferredNode], state: State, errors: Errors

) -> None:

"""Check type arguments against type variable bounds and restrictions.

This mirrors the logic in check_type_arguments() except that we process only

some targets. This is used in fine grained incremental mode.

"""

analyzer = TypeArgumentAnalyzer(

errors,

state.options,

is_typeshed_file(state.options.abs_custom_typeshed_dir, state.path or ""),

state.manager.semantic_analyzer.named_type,

)

with state.wrap_context():

with mypy.state.state.strict_optional_set(state.options.strict_optional):

for target in targets:

func: FuncDef | OverloadedFuncDef | None = None

if isinstance(target.node, (FuncDef, OverloadedFuncDef)):

func = target.node

saved = (state.id, target.active_typeinfo, func) # module, class, function

with errors.scope.saved_scope(saved) if errors.scope else nullcontext():

analyzer.recurse_into_functions = func is not None

target.node.accept(analyzer)

def apply_class_plugin_hooks(graph: Graph, scc: list[str], errors: Errors) -> None:

"""Apply class plugin hooks within a SCC.

We run these after to the main semantic analysis so that the hooks

don't need to deal with incomplete definitions such as placeholder

types.

Note that some hooks incorrectly run during the main semantic

analysis pass, for historical reasons.

"""

num_passes = 0

incomplete = True

# If we encounter a base class that has not been processed, we'll run another

# pass. This should eventually reach a fixed point.

while incomplete:

assert num_passes < 10, "Internal error: too many class plugin hook passes"

num_passes += 1

incomplete = False

for module in scc:

state = graph[module]

tree = state.tree

assert tree

for _, node, _ in tree.local_definitions():

if isinstance(node.node, TypeInfo):

if not apply_hooks_to_class(

state.manager.semantic_analyzer,

module,

node.node,

state.options,

tree,

errors,

):

incomplete = True

def apply_hooks_to_class(

self: SemanticAnalyzer,

module: str,

info: TypeInfo,

options: Options,

file_node: MypyFile,

errors: Errors,

) -> bool:

# TODO: Move more class-related hooks here?

defn = info.defn

ok = True

for decorator in defn.decorators:

with self.file_context(file_node, options, info):

hook = None

decorator_name = self.get_fullname_for_hook(decorator)

if decorator_name:

hook = self.plugin.get_class_decorator_hook_2(decorator_name)

# Special case: if the decorator is itself decorated with

# typing.dataclass_transform, apply the hook for the dataclasses plugin

# TODO: remove special casing here

if hook is None and find_dataclass_transform_spec(decorator):

hook = dataclasses_plugin.dataclass_class_maker_callback

if hook:

ok = ok and hook(ClassDefContext(defn, decorator, self))

# Check if the class definition itself triggers a dataclass transform (via a parent class/

# metaclass)

spec = find_dataclass_transform_spec(info)

if spec is not None:

with self.file_context(file_node, options, info):

# We can't use the normal hook because reason = defn, and ClassDefContext only accepts

# an Expression for reason

ok = ok and dataclasses_plugin.DataclassTransformer(defn, defn, spec, self).transform()

return ok

def calculate_class_properties(graph: Graph, scc: list[str], errors: Errors) -> None:

builtins = graph["builtins"].tree

assert builtins

for module in scc:

state = graph[module]

tree = state.tree

assert tree

for _, node, _ in tree.local_definitions():

if isinstance(node.node, TypeInfo):

with state.manager.semantic_analyzer.file_context(tree, state.options, node.node):

calculate_class_abstract_status(node.node, tree.is_stub, errors)

check_protocol_status(node.node, errors)

calculate_class_vars(node.node)

add_type_promotion(

node.node, tree.names, graph[module].options, builtins.names

)

def check_blockers(graph: Graph, scc: list[str]) -> None:

for module in scc:

graph[module].check_blockers()