f20080305d
Определение специфических рекомендаций (матрица профилей §8):
1. Ж/д слой (закрыт мёртвый railway ×2.5 у РАС):
- /api/v1/water/{case_id} отдаёт railway=rail как LineString
(без service/industrial/military веток), кэш общий v2;
- railway_warning «перекрыть/проверить немедленно» по профилям;
- SearchMap: Polyline слой ж/д (тёмно-красный), счётчики 💧/🚂.
2. cant_swim → профиль не_умеет_плавать (water ×3.0, без изменения
радиуса, critical_warning «обследовать водоёмы НЕМЕДЛЕННО»):
- раньше чекбокс влиял только на текст, в скоринге был пробел;
- derive в analyze._derive_profiles — работает и для closed_cases.
3. unmodeled_profiles: ДЦП/слабое зрение/слух — честная пометка
«вне поведенческой модели» с пояснением (vector_tasks B12:
профили без аналога не выдавать за учтённые); блок на фронте
в карточке здоровья.
Площадь воды: сферический эксцесс, проверен на квадрате 53° (744017 м²
vs 743272 точного). Тесты: 202 passed (новый test_cant_swim_profile).
225 lines
7.4 KiB
Python
225 lines
7.4 KiB
Python
from __future__ import annotations as _annotations
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import typing
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from copy import deepcopy
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from enum import Enum
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from typing import Any
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import typing_extensions
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from .._internal import (
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_model_construction,
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_typing_extra,
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_utils,
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)
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if typing.TYPE_CHECKING:
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from .. import BaseModel
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from .._internal._utils import AbstractSetIntStr, MappingIntStrAny
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AnyClassMethod = classmethod[Any, Any, Any]
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TupleGenerator = typing.Generator[tuple[str, Any], None, None]
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Model = typing.TypeVar('Model', bound='BaseModel')
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# should be `set[int] | set[str] | dict[int, IncEx] | dict[str, IncEx] | None`, but mypy can't cope
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IncEx: typing_extensions.TypeAlias = 'set[int] | set[str] | dict[int, Any] | dict[str, Any] | None'
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_object_setattr = _model_construction.object_setattr
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def _iter(
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self: BaseModel,
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to_dict: bool = False,
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by_alias: bool = False,
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include: AbstractSetIntStr | MappingIntStrAny | None = None,
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exclude: AbstractSetIntStr | MappingIntStrAny | None = None,
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exclude_unset: bool = False,
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exclude_defaults: bool = False,
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exclude_none: bool = False,
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) -> TupleGenerator:
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# Merge field set excludes with explicit exclude parameter with explicit overriding field set options.
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# The extra "is not None" guards are not logically necessary but optimizes performance for the simple case.
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if exclude is not None:
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exclude = _utils.ValueItems.merge(
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{k: v.exclude for k, v in self.__pydantic_fields__.items() if v.exclude is not None}, exclude
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)
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if include is not None:
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include = _utils.ValueItems.merge(dict.fromkeys(self.__pydantic_fields__, True), include, intersect=True)
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allowed_keys = _calculate_keys(self, include=include, exclude=exclude, exclude_unset=exclude_unset) # type: ignore
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if allowed_keys is None and not (to_dict or by_alias or exclude_unset or exclude_defaults or exclude_none):
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# huge boost for plain _iter()
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yield from self.__dict__.items()
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if self.__pydantic_extra__:
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yield from self.__pydantic_extra__.items()
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return
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value_exclude = _utils.ValueItems(self, exclude) if exclude is not None else None
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value_include = _utils.ValueItems(self, include) if include is not None else None
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if self.__pydantic_extra__ is None:
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items = self.__dict__.items()
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else:
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items = list(self.__dict__.items()) + list(self.__pydantic_extra__.items())
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for field_key, v in items:
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if (allowed_keys is not None and field_key not in allowed_keys) or (exclude_none and v is None):
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continue
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if exclude_defaults:
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try:
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field = self.__pydantic_fields__[field_key]
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except KeyError:
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pass
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else:
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if not field.is_required() and field.default == v:
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continue
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if by_alias and field_key in self.__pydantic_fields__:
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dict_key = self.__pydantic_fields__[field_key].alias or field_key
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else:
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dict_key = field_key
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if to_dict or value_include or value_exclude:
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v = _get_value(
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type(self),
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v,
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to_dict=to_dict,
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by_alias=by_alias,
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include=value_include and value_include.for_element(field_key),
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exclude=value_exclude and value_exclude.for_element(field_key),
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exclude_unset=exclude_unset,
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exclude_defaults=exclude_defaults,
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exclude_none=exclude_none,
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)
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yield dict_key, v
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def _copy_and_set_values(
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self: Model,
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values: dict[str, Any],
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fields_set: set[str],
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extra: dict[str, Any] | None = None,
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private: dict[str, Any] | None = None,
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*,
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deep: bool, # UP006
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) -> Model:
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if deep:
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# chances of having empty dict here are quite low for using smart_deepcopy
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values = deepcopy(values)
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extra = deepcopy(extra)
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private = deepcopy(private)
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cls = self.__class__
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m = cls.__new__(cls)
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_object_setattr(m, '__dict__', values)
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_object_setattr(m, '__pydantic_extra__', extra)
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_object_setattr(m, '__pydantic_fields_set__', fields_set)
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_object_setattr(m, '__pydantic_private__', private)
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return m
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@typing.no_type_check
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def _get_value(
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cls: type[BaseModel],
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v: Any,
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to_dict: bool,
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by_alias: bool,
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include: AbstractSetIntStr | MappingIntStrAny | None,
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exclude: AbstractSetIntStr | MappingIntStrAny | None,
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exclude_unset: bool,
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exclude_defaults: bool,
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exclude_none: bool,
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) -> Any:
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from .. import BaseModel
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if isinstance(v, BaseModel):
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if to_dict:
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return v.model_dump(
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by_alias=by_alias,
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exclude_unset=exclude_unset,
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exclude_defaults=exclude_defaults,
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include=include, # type: ignore
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exclude=exclude, # type: ignore
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exclude_none=exclude_none,
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)
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else:
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return v.copy(include=include, exclude=exclude)
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value_exclude = _utils.ValueItems(v, exclude) if exclude else None
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value_include = _utils.ValueItems(v, include) if include else None
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if isinstance(v, dict):
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return {
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k_: _get_value(
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cls,
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v_,
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to_dict=to_dict,
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by_alias=by_alias,
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exclude_unset=exclude_unset,
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exclude_defaults=exclude_defaults,
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include=value_include and value_include.for_element(k_),
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exclude=value_exclude and value_exclude.for_element(k_),
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exclude_none=exclude_none,
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)
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for k_, v_ in v.items()
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if (not value_exclude or not value_exclude.is_excluded(k_))
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and (not value_include or value_include.is_included(k_))
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}
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elif _utils.sequence_like(v):
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seq_args = (
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_get_value(
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cls,
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v_,
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to_dict=to_dict,
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by_alias=by_alias,
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exclude_unset=exclude_unset,
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exclude_defaults=exclude_defaults,
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include=value_include and value_include.for_element(i),
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exclude=value_exclude and value_exclude.for_element(i),
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exclude_none=exclude_none,
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)
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for i, v_ in enumerate(v)
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if (not value_exclude or not value_exclude.is_excluded(i))
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and (not value_include or value_include.is_included(i))
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)
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return v.__class__(*seq_args) if _typing_extra.is_namedtuple(v.__class__) else v.__class__(seq_args)
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elif isinstance(v, Enum) and getattr(cls.model_config, 'use_enum_values', False):
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return v.value
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else:
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return v
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def _calculate_keys(
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self: BaseModel,
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include: MappingIntStrAny | None,
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exclude: MappingIntStrAny | None,
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exclude_unset: bool,
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update: dict[str, Any] | None = None, # noqa UP006
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) -> typing.AbstractSet[str] | None:
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if include is None and exclude is None and exclude_unset is False:
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return None
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keys: typing.AbstractSet[str]
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if exclude_unset:
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keys = self.__pydantic_fields_set__.copy()
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else:
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keys = set(self.__dict__.keys())
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keys = keys | (self.__pydantic_extra__ or {}).keys()
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if include is not None:
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keys &= include.keys()
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if update:
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keys -= update.keys()
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if exclude:
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keys -= {k for k, v in exclude.items() if _utils.ValueItems.is_true(v)}
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return keys
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