"""Plugin system for extending mypy.

At large scale the plugin system works as following:

* Plugins are collected from the corresponding mypy config file option

(either via paths to Python files, or installed Python modules)

and imported using importlib.

* Every module should get an entry point function (called 'plugin' by default,

but may be overridden in the config file) that should accept a single string

argument that is a full mypy version (includes git commit hash for dev

versions) and return a subclass of mypy.plugins.Plugin.

* All plugin class constructors should match the signature of mypy.plugin.Plugin

(i.e. should accept an mypy.options.Options object), and *must* call

super().__init__().

* At several steps during semantic analysis and type checking mypy calls

special `get_xxx` methods on user plugins with a single string argument that

is a fully qualified name (full name) of a relevant definition

(see mypy.plugin.Plugin method docstrings for details).

* The plugins are called in the order they are passed in the config option.

Every plugin must decide whether to act on a given full name. The first

plugin that returns non-None object will be used.

* The above decision should be made using the limited common API specified by

mypy.plugin.CommonPluginApi.

* The callback returned by the plugin will be called with a larger context that

includes relevant current state (e.g. a default return type, or a default

attribute type) and a wider relevant API provider (e.g.

SemanticAnalyzerPluginInterface or CheckerPluginInterface).

* The result of this is used for further processing. See various `XxxContext`

named tuples for details about which information is given to each hook.

Plugin developers should ensure that their plugins work well in incremental and

daemon modes. In particular, plugins should not hold global state, and should

always call add_plugin_dependency() in plugin hooks called during semantic

analysis. See the method docstring for more details.

There is no dedicated cache storage for plugins, but plugins can store

per-TypeInfo data in a special .metadata attribute that is serialized to the

mypy caches between incremental runs. To avoid collisions between plugins, they

are encouraged to store their state under a dedicated key coinciding with

plugin name in the metadata dictionary. Every value stored there must be

JSON-serializable.

## Notes about the semantic analyzer

Mypy 0.710 introduced a new semantic analyzer that changed how plugins are

expected to work in several notable ways (from mypy 0.730 the old semantic

analyzer is no longer available):

1. The order of processing AST nodes in modules is different. The old semantic

analyzer processed modules in textual order, one module at a time. The new

semantic analyzer first processes the module top levels, including bodies of

any top-level classes and classes nested within classes. ("Top-level" here

means "not nested within a function/method".) Functions and methods are

processed only after module top levels have been finished. If there is an

import cycle, all module top levels in the cycle are processed before

processing any functions or methods. Each unit of processing (a module top

level or a function/method) is called a *target*.

This also means that function signatures in the same module have not been

analyzed yet when analyzing the module top level. If you need access to

a function signature, you'll need to explicitly analyze the signature first

using `anal_type()`.

2. Each target can be processed multiple times. This may happen if some forward

references are not ready yet, for example. This means that semantic analyzer

related plugin hooks can be called multiple times for the same full name.

These plugin methods must thus be idempotent.

3. The `anal_type` API function returns None if some part of the type is not

available yet. If this happens, the current target being analyzed will be

*deferred*, which means that it will be processed again soon, in the hope

that additional dependencies will be available. This may happen if there are

forward references to types or inter-module references to types within an

import cycle.

Note that if there is a circular definition, mypy may decide to stop

processing to avoid an infinite number of iterations. When this happens,

`anal_type` will generate an error and return an `AnyType` type object

during the final iteration (instead of None).

4. There is a new API method `defer()`. This can be used to explicitly request

the current target to be reprocessed one more time. You don't need this

to call this if `anal_type` returns None, however.

5. There is a new API property `final_iteration`, which is true once mypy

detected no progress during the previous iteration or if the maximum

semantic analysis iteration count has been reached. You must never

defer during the final iteration, as it will cause a crash.

6. The `node` attribute of SymbolTableNode objects may contain a reference to

a PlaceholderNode object. This object means that this definition has not

been fully processed yet. If you encounter a PlaceholderNode, you should

defer unless it's the final iteration. If it's the final iteration, you

should generate an error message. It usually means that there's a cyclic

definition that cannot be resolved by mypy. PlaceholderNodes can only refer

to references inside an import cycle. If you are looking up things from

another module, such as the builtins, that is outside the current module or

import cycle, you can safely assume that you won't receive a placeholder.

When testing your plugin, you should have a test case that forces a module top

level to be processed multiple times. The easiest way to do this is to include

a forward reference to a class in a top-level annotation. Example:

c: C # Forward reference causes second analysis pass

class C: pass

Note that a forward reference in a function signature won't trigger another

pass, since all functions are processed only after the top level has been fully

analyzed.

"""

from __future__ import annotations

from abc import abstractmethod

from collections.abc import Callable

from typing import TYPE_CHECKING, Any, NamedTuple, TypeVar

from mypy_extensions import mypyc_attr, trait

from mypy.errorcodes import ErrorCode

from mypy.errors import ErrorInfo

from mypy.lookup import lookup_fully_qualified

from mypy.message_registry import ErrorMessage

from mypy.nodes import (

ArgKind,

CallExpr,

ClassDef,

Context,

Expression,

MypyFile,

SymbolTableNode,

TypeInfo,

)

from mypy.options import Options

from mypy.types import (

CallableType,

FunctionLike,

Instance,

ProperType,

Type,

TypeList,

UnboundType,

)

if TYPE_CHECKING:

from mypy.messages import MessageBuilder

from mypy.tvar_scope import TypeVarLikeScope

@trait

class TypeAnalyzerPluginInterface:

"""Interface for accessing semantic analyzer functionality in plugins.

Methods docstrings contain only basic info. Look for corresponding implementation

docstrings in typeanal.py for more details.

"""

# An options object. Note: these are the cloned options for the current file.

# This might be different from Plugin.options (that contains default/global options)

# if there are per-file options in the config. This applies to all other interfaces

# in this file.

options: Options

@abstractmethod

def fail(self, msg: str, ctx: Context, *, code: ErrorCode | None = None) -> None:

"""Emit an error message at given location."""

raise NotImplementedError

@abstractmethod

def named_type(self, fullname: str, args: list[Type], /) -> Instance:

"""Construct an instance of a builtin type with given name."""

raise NotImplementedError

@abstractmethod

def analyze_type(self, typ: Type, /) -> Type:

"""Analyze an unbound type using the default mypy logic."""

raise NotImplementedError

@abstractmethod

def analyze_callable_args(

self, arglist: TypeList

) -> tuple[list[Type], list[ArgKind], list[str | None]] | None:

"""Find types, kinds, and names of arguments from extended callable syntax."""

raise NotImplementedError

# A context for a hook that semantically analyzes an unbound type.

class AnalyzeTypeContext(NamedTuple):

type: UnboundType # Type to analyze

context: Context # Relevant location context (e.g. for error messages)

api: TypeAnalyzerPluginInterface

@mypyc_attr(allow_interpreted_subclasses=True)

class CommonPluginApi:

"""

A common plugin API (shared between semantic analysis and type checking phases)

that all plugin hooks get independently of the context.

"""

# Global mypy options.

# Per-file options can be only accessed on various

# XxxPluginInterface classes.

options: Options

@abstractmethod

def lookup_fully_qualified(self, fullname: str) -> SymbolTableNode | None:

"""Lookup a symbol by its full name (including module).

This lookup function available for all plugins. Return None if a name

is not found. This function doesn't support lookup from current scope.

Use SemanticAnalyzerPluginInterface.lookup_qualified() for this."""

raise NotImplementedError

@trait

class CheckerPluginInterface:

"""Interface for accessing type checker functionality in plugins.

Methods docstrings contain only basic info. Look for corresponding implementation

docstrings in checker.py for more details.

"""

msg: MessageBuilder

options: Options

path: str

# Type context for type inference

@property

@abstractmethod

def type_context(self) -> list[Type | None]:

"""Return the type context of the plugin"""

raise NotImplementedError

@abstractmethod

def fail(

self, msg: str | ErrorMessage, ctx: Context, /, *, code: ErrorCode | None = None

) -> ErrorInfo | None:

"""Emit an error message at given location."""

raise NotImplementedError

@abstractmethod

def named_generic_type(self, name: str, args: list[Type]) -> Instance:

"""Construct an instance of a generic type with given type arguments."""

raise NotImplementedError

@abstractmethod

def get_expression_type(self, node: Expression, type_context: Type | None = None) -> Type:

"""Checks the type of the given expression."""

raise NotImplementedError

@trait

class SemanticAnalyzerPluginInterface:

"""Interface for accessing semantic analyzer functionality in plugins.

Methods docstrings contain only basic info. Look for corresponding implementation

docstrings in semanal.py for more details.

# TODO: clean-up lookup functions.

"""

modules: dict[str, MypyFile]

# Options for current file.

options: Options

cur_mod_id: str

msg: MessageBuilder

@abstractmethod

def named_type(self, fullname: str, args: list[Type] | None = None) -> Instance:

"""Construct an instance of a builtin type with given type arguments."""

raise NotImplementedError

@abstractmethod

def builtin_type(self, fully_qualified_name: str) -> Instance:

"""Legacy function -- use named_type() instead."""

# NOTE: Do not delete this since many plugins may still use it.

raise NotImplementedError

@abstractmethod

def named_type_or_none(self, fullname: str, args: list[Type] | None = None) -> Instance | None:

"""Construct an instance of a type with given type arguments.

Return None if a type could not be constructed for the qualified

type name. This is possible when the qualified name includes a

module name and the module has not been imported.

"""

raise NotImplementedError

@abstractmethod

def basic_new_typeinfo(self, name: str, basetype_or_fallback: Instance, line: int) -> TypeInfo:

raise NotImplementedError

@abstractmethod

def parse_bool(self, expr: Expression) -> bool | None:

"""Parse True/False literals."""

raise NotImplementedError

@abstractmethod

def parse_str_literal(self, expr: Expression) -> str | None:

"""Parse string literals."""

@abstractmethod

def fail(

self,

msg: str,

ctx: Context,

serious: bool = False,

*,

blocker: bool = False,

code: ErrorCode | None = None,

) -> None:

"""Emit an error message at given location."""

raise NotImplementedError

@abstractmethod

def anal_type(

self,

typ: Type,

/,

*,

tvar_scope: TypeVarLikeScope | None = None,

allow_tuple_literal: bool = False,

allow_unbound_tvars: bool = False,

report_invalid_types: bool = True,

) -> Type | None:

"""Analyze an unbound type.

Return None if some part of the type is not ready yet. In this

case the current target being analyzed will be deferred and

analyzed again.

"""

raise NotImplementedError

@abstractmethod

def class_type(self, self_type: Type) -> Type:

"""Generate type of first argument of class methods from type of self."""

raise NotImplementedError

@abstractmethod

def lookup_fully_qualified(self, fullname: str, /) -> SymbolTableNode:

"""Lookup a symbol by its fully qualified name.

Raise an error if not found.

"""

raise NotImplementedError

@abstractmethod

def lookup_fully_qualified_or_none(self, fullname: str, /) -> SymbolTableNode | None:

"""Lookup a symbol by its fully qualified name.

Return None if not found.

"""

raise NotImplementedError

@abstractmethod

def lookup_qualified(

self, name: str, ctx: Context, suppress_errors: bool = False

) -> SymbolTableNode | None:

"""Lookup symbol using a name in current scope.

This follows Python local->non-local->global->builtins rules.

"""

raise NotImplementedError

@abstractmethod

def add_plugin_dependency(self, trigger: str, target: str | None = None) -> None:

"""Specify semantic dependencies for generated methods/variables.

If the symbol with full name given by trigger is found to be stale by mypy,

then the body of node with full name given by target will be re-checked.

By default, this is the node that is currently analyzed.

For example, the dataclass plugin adds a generated __init__ method with

a signature that depends on types of attributes in ancestor classes. If any

attribute in an ancestor class gets stale (modified), we need to reprocess

the subclasses (and thus regenerate __init__ methods).

This is used by fine-grained incremental mode (mypy daemon). See mypy/server/deps.py

for more details.

"""

raise NotImplementedError

@abstractmethod

def add_symbol_table_node(self, name: str, symbol: SymbolTableNode) -> Any:

"""Add node to global symbol table (or to nearest class if there is one)."""

raise NotImplementedError

@abstractmethod

def qualified_name(self, name: str) -> str:

"""Make qualified name using current module and enclosing class (if any)."""

raise NotImplementedError

@abstractmethod

def defer(self) -> None:

"""Call this to defer the processing of the current node.

This will request an additional iteration of semantic analysis.

"""

raise NotImplementedError

@property

@abstractmethod

def final_iteration(self) -> bool:

"""Is this the final iteration of semantic analysis?"""

raise NotImplementedError

@property

@abstractmethod

def is_stub_file(self) -> bool:

raise NotImplementedError

@abstractmethod

def analyze_simple_literal_type(self, rvalue: Expression, is_final: bool) -> Type | None:

raise NotImplementedError

# A context for querying for configuration data about a module for

# cache invalidation purposes.

class ReportConfigContext(NamedTuple):

id: str # Module name

path: str # Module file path

is_check: bool # Is this invocation for checking whether the config matches

# A context for a function signature hook that infers a better signature for a

# function. Note that argument types aren't available yet. If you need them,

# you have to use a method hook instead.

class FunctionSigContext(NamedTuple):

args: list[list[Expression]] # Actual expressions for each formal argument

default_signature: CallableType # Original signature of the method

context: Context # Relevant location context (e.g. for error messages)

api: CheckerPluginInterface

# A context for a function hook that infers the return type of a function with

# a special signature.

#

# A no-op callback would just return the inferred return type, but a useful

# callback at least sometimes can infer a more precise type.

class FunctionContext(NamedTuple):

arg_types: list[list[Type]] # List of actual caller types for each formal argument

arg_kinds: list[list[ArgKind]] # Ditto for argument kinds, see nodes.ARG_* constants

# Names of formal parameters from the callee definition,

# these will be sufficient in most cases.

callee_arg_names: list[str | None]

# Names of actual arguments in the call expression. For example,

# in a situation like this:

# def func(**kwargs) -> None:

# pass

# func(kw1=1, kw2=2)

# callee_arg_names will be ['kwargs'] and arg_names will be [['kw1', 'kw2']].

arg_names: list[list[str | None]]

default_return_type: Type # Return type inferred from signature

args: list[list[Expression]] # Actual expressions for each formal argument

context: Context # Relevant location context (e.g. for error messages)

api: CheckerPluginInterface

# A context for a method signature hook that infers a better signature for a

# method. Note that argument types aren't available yet. If you need them,

# you have to use a method hook instead.

# TODO: document ProperType in the plugin changelog/update issue.

class MethodSigContext(NamedTuple):

type: ProperType # Base object type for method call

args: list[list[Expression]] # Actual expressions for each formal argument

default_signature: CallableType # Original signature of the method

context: Context # Relevant location context (e.g. for error messages)

api: CheckerPluginInterface

# A context for a method hook that infers the return type of a method with a

# special signature.

#

# This is very similar to FunctionContext (only differences are documented).

class MethodContext(NamedTuple):

type: ProperType # Base object type for method call

arg_types: list[list[Type]] # List of actual caller types for each formal argument

# see FunctionContext for details about names and kinds

arg_kinds: list[list[ArgKind]]

callee_arg_names: list[str | None]

arg_names: list[list[str | None]]

default_return_type: Type # Return type inferred by mypy

args: list[list[Expression]] # Lists of actual expressions for every formal argument

context: Context

api: CheckerPluginInterface

# A context for an attribute type hook that infers the type of an attribute.

class AttributeContext(NamedTuple):

type: ProperType # Type of object with attribute

default_attr_type: Type # Original attribute type

is_lvalue: bool # Whether the attribute is the target of an assignment

context: Context # Relevant location context (e.g. for error messages)

api: CheckerPluginInterface

# A context for a class hook that modifies the class definition.

class ClassDefContext(NamedTuple):

cls: ClassDef # The class definition

reason: Expression # The expression being applied (decorator, metaclass, base class)

api: SemanticAnalyzerPluginInterface

# A context for dynamic class definitions like

# Base = declarative_base()

class DynamicClassDefContext(NamedTuple):

call: CallExpr # The r.h.s. of dynamic class definition

name: str # The name this class is being assigned to

api: SemanticAnalyzerPluginInterface

@mypyc_attr(allow_interpreted_subclasses=True)

class Plugin(CommonPluginApi):

"""Base class of all type checker plugins.

This defines a no-op plugin. Subclasses can override some methods to

provide some actual functionality.

All get_ methods are treated as pure functions (you should assume that

results might be cached). A plugin should return None from a get_ method

to give way to other plugins.

Look at the comments of various *Context objects for additional information on

various hooks.

"""

def __init__(self, options: Options) -> None:

self.options = options

self.python_version = options.python_version

# This can't be set in __init__ because it is executed too soon in build.py.

# Therefore, build.py *must* set it later before graph processing starts

# by calling set_modules().

self._modules: dict[str, MypyFile] | None = None

def set_modules(self, modules: dict[str, MypyFile]) -> None:

self._modules = modules

def lookup_fully_qualified(self, fullname: str) -> SymbolTableNode | None:

assert self._modules is not None

return lookup_fully_qualified(fullname, self._modules)

def report_config_data(self, ctx: ReportConfigContext) -> Any:

"""Get representation of configuration data for a module.

The data must be encodable as JSON and will be stored in the

cache metadata for the module. A mismatch between the cached

values and the returned will result in that module's cache

being invalidated and the module being rechecked.

This can be called twice for each module, once after loading

the cache to check if it is valid and once while writing new

cache information.

If is_check in the context is true, then the return of this

call will be checked against the cached version. Otherwise the

call is being made to determine what to put in the cache. This

can be used to allow consulting extra cache files in certain

complex situations.

This can be used to incorporate external configuration information

that might require changes to typechecking.

"""

return None

def get_additional_deps(self, file: MypyFile) -> list[tuple[int, str, int]]:

"""Customize dependencies for a module.

This hook allows adding in new dependencies for a module. It

is called after parsing a file but before analysis. This can

be useful if a library has dependencies that are dynamic based

on configuration information, for example.

Returns a list of (priority, module name, line number) tuples.

The line number can be -1 when there is not a known real line number.

Priorities are defined in mypy.build (but maybe shouldn't be).

10 is a good choice for priority.

"""

return []

def get_type_analyze_hook(self, fullname: str) -> Callable[[AnalyzeTypeContext], Type] | None:

"""Customize behaviour of the type analyzer for given full names.

This method is called during the semantic analysis pass whenever mypy sees an

unbound type. For example, while analysing this code:

from lib import Special, Other

var: Special

def func(x: Other[int]) -> None:

...

this method will be called with 'lib.Special', and then with 'lib.Other'.

The callback returned by plugin must return an analyzed type,

i.e. an instance of `mypy.types.Type`.

"""

return None

def get_function_signature_hook(

self, fullname: str

) -> Callable[[FunctionSigContext], FunctionLike] | None:

"""Adjust the signature of a function.

This method is called before type checking a function call. Plugin

may infer a better type for the function.

from lib import Class, do_stuff

do_stuff(42)

Class()

This method will be called with 'lib.do_stuff' and then with 'lib.Class'.

"""

return None

def get_function_hook(self, fullname: str) -> Callable[[FunctionContext], Type] | None:

"""Adjust the return type of a function call.

This method is called after type checking a call. Plugin may adjust the return

type inferred by mypy, and/or emit some error messages. Note, this hook is also

called for class instantiation calls, so that in this example:

from lib import Class, do_stuff

do_stuff(42)

Class()

This method will be called with 'lib.do_stuff' and then with 'lib.Class'.

"""

return None

def get_method_signature_hook(

self, fullname: str

) -> Callable[[MethodSigContext], FunctionLike] | None:

"""Adjust the signature of a method.

This method is called before type checking a method call. Plugin

may infer a better type for the method. The hook is also called for special

Python dunder methods except __init__ and __new__ (use get_function_hook to customize

class instantiation). This function is called with the method full name using

the class where it was _defined_. For example, in this code:

from lib import Special

class Base:

def method(self, arg: Any) -> Any:

...

class Derived(Base):

...

var: Derived

var.method(42)

x: Special

y = x[0]

this method is called with '__main__.Base.method', and then with

'lib.Special.__getitem__'.

"""

return None

def get_method_hook(self, fullname: str) -> Callable[[MethodContext], Type] | None:

"""Adjust return type of a method call.

This is the same as get_function_hook(), but is called with the

method full name (again, using the class where the method is defined).

"""

return None

def get_attribute_hook(self, fullname: str) -> Callable[[AttributeContext], Type] | None:

"""Adjust type of an instance attribute.

This method is called with attribute full name using the class of the instance where

the attribute was defined (or Var.info.fullname for generated attributes).

For classes without __getattr__ or __getattribute__, this hook is only called for

names of fields/properties (but not methods) that exist in the instance MRO.

For classes that implement __getattr__ or __getattribute__, this hook is called

for all fields/properties, including nonexistent ones (but still not methods).

For example:

class Base:

x: Any

def __getattr__(self, attr: str) -> Any: ...

class Derived(Base):

...

var: Derived

var.x

var.y

get_attribute_hook is called with '__main__.Base.x' and '__main__.Base.y'.

However, if we had not implemented __getattr__ on Base, you would only get

the callback for 'var.x'; 'var.y' would produce an error without calling the hook.

"""

return None

def get_class_attribute_hook(self, fullname: str) -> Callable[[AttributeContext], Type] | None:

"""

Adjust type of a class attribute.

This method is called with attribute full name using the class where the attribute was

defined (or Var.info.fullname for generated attributes).

For example:

class Cls:

x: Any

Cls.x

get_class_attribute_hook is called with '__main__.Cls.x' as fullname.

"""

return None

def get_class_decorator_hook(self, fullname: str) -> Callable[[ClassDefContext], None] | None:

"""Update class definition for given class decorators.

The plugin can modify a TypeInfo _in place_ (for example add some generated

methods to the symbol table). This hook is called after the class body was

semantically analyzed, but *there may still be placeholders* (typically

caused by forward references).

NOTE: Usually get_class_decorator_hook_2 is the better option, since it

guarantees that there are no placeholders.

The hook is called with full names of all class decorators.

The hook can be called multiple times per class, so it must be

idempotent.

"""

return None

def get_class_decorator_hook_2(

self, fullname: str

) -> Callable[[ClassDefContext], bool] | None:

"""Update class definition for given class decorators.

Similar to get_class_decorator_hook, but this runs in a later pass when

placeholders have been resolved.

The hook can return False if some base class hasn't been

processed yet using class hooks. It causes all class hooks

(that are run in this same pass) to be invoked another time for

the file(s) currently being processed.

The hook can be called multiple times per class, so it must be

idempotent.

"""

return None

def get_metaclass_hook(self, fullname: str) -> Callable[[ClassDefContext], None] | None:

"""Update class definition for given declared metaclasses.

Same as get_class_decorator_hook() but for metaclasses. Note:

this hook will be only called for explicit metaclasses, not for

inherited ones.

TODO: probably it should also be called on inherited metaclasses.

"""

return None

def get_base_class_hook(self, fullname: str) -> Callable[[ClassDefContext], None] | None:

"""Update class definition for given base classes.

Same as get_class_decorator_hook() but for base classes. Base classes

don't need to refer to TypeInfos, if a base class refers to a variable with

Any type, this hook will still be called.

"""

return None

def get_customize_class_mro_hook(

self, fullname: str

) -> Callable[[ClassDefContext], None] | None:

"""Customize MRO for given classes.

The plugin can modify the class MRO _in place_. This method is called

with the class full name before its body was semantically analyzed.

"""

return None

def get_dynamic_class_hook(

self, fullname: str

) -> Callable[[DynamicClassDefContext], None] | None:

"""Semantically analyze a dynamic class definition.

This plugin hook allows one to semantically analyze dynamic class definitions like:

from lib import dynamic_class

X = dynamic_class('X', [])

For such definition, this hook will be called with 'lib.dynamic_class'.

The plugin should create the corresponding TypeInfo, and place it into a relevant

symbol table, e.g. using ctx.api.add_symbol_table_node().

"""

return None

T = TypeVar("T")

class ChainedPlugin(Plugin):

"""A plugin that represents a sequence of chained plugins.

Each lookup method returns the hook for the first plugin that

reports a match.

This class should not be subclassed -- use Plugin as the base class

for all plugins.

"""

# TODO: Support caching of lookup results (through a LRU cache, for example).

def __init__(self, options: Options, plugins: list[Plugin]) -> None:

"""Initialize chained plugin.

Assume that the child plugins aren't mutated (results may be cached).

"""

super().__init__(options)

self._plugins = plugins

def set_modules(self, modules: dict[str, MypyFile]) -> None:

for plugin in self._plugins:

plugin.set_modules(modules)

def report_config_data(self, ctx: ReportConfigContext) -> Any:

config_data = [plugin.report_config_data(ctx) for plugin in self._plugins]

return config_data if any(x is not None for x in config_data) else None

def get_additional_deps(self, file: MypyFile) -> list[tuple[int, str, int]]:

deps = []

for plugin in self._plugins:

deps.extend(plugin.get_additional_deps(file))

return deps

def get_type_analyze_hook(self, fullname: str) -> Callable[[AnalyzeTypeContext], Type] | None:

# Micro-optimization: Inline iteration over plugins

for plugin in self._plugins:

hook = plugin.get_type_analyze_hook(fullname)

if hook is not None:

return hook

return None

def get_function_signature_hook(

self, fullname: str

) -> Callable[[FunctionSigContext], FunctionLike] | None:

# Micro-optimization: Inline iteration over plugins

for plugin in self._plugins:

hook = plugin.get_function_signature_hook(fullname)

if hook is not None:

return hook

return None

def get_function_hook(self, fullname: str) -> Callable[[FunctionContext], Type] | None:

return self._find_hook(lambda plugin: plugin.get_function_hook(fullname))

def get_method_signature_hook(

self, fullname: str

) -> Callable[[MethodSigContext], FunctionLike] | None:

# Micro-optimization: Inline iteration over plugins

for plugin in self._plugins:

hook = plugin.get_method_signature_hook(fullname)

if hook is not None:

return hook

return None

def get_method_hook(self, fullname: str) -> Callable[[MethodContext], Type] | None:

# Micro-optimization: Inline iteration over plugins

for plugin in self._plugins:

hook = plugin.get_method_hook(fullname)

if hook is not None:

return hook

return None

def get_attribute_hook(self, fullname: str) -> Callable[[AttributeContext], Type] | None:

# Micro-optimization: Inline iteration over plugins

for plugin in self._plugins:

hook = plugin.get_attribute_hook(fullname)

if hook is not None:

return hook

return None

def get_class_attribute_hook(self, fullname: str) -> Callable[[AttributeContext], Type] | None:

return self._find_hook(lambda plugin: plugin.get_class_attribute_hook(fullname))

def get_class_decorator_hook(self, fullname: str) -> Callable[[ClassDefContext], None] | None:

return self._find_hook(lambda plugin: plugin.get_class_decorator_hook(fullname))

def get_class_decorator_hook_2(

self, fullname: str

) -> Callable[[ClassDefContext], bool] | None:

return self._find_hook(lambda plugin: plugin.get_class_decorator_hook_2(fullname))

def get_metaclass_hook(self, fullname: str) -> Callable[[ClassDefContext], None] | None:

return self._find_hook(lambda plugin: plugin.get_metaclass_hook(fullname))

def get_base_class_hook(self, fullname: str) -> Callable[[ClassDefContext], None] | None:

return self._find_hook(lambda plugin: plugin.get_base_class_hook(fullname))

def get_customize_class_mro_hook(

self, fullname: str

) -> Callable[[ClassDefContext], None] | None:

return self._find_hook(lambda plugin: plugin.get_customize_class_mro_hook(fullname))

def get_dynamic_class_hook(

self, fullname: str

) -> Callable[[DynamicClassDefContext], None] | None:

return self._find_hook(lambda plugin: plugin.get_dynamic_class_hook(fullname))

def _find_hook(self, lookup: Callable[[Plugin], T]) -> T | None:

for plugin in self._plugins:

hook = lookup(plugin)

if hook is not None:

return hook

return None