marcocosta10000
6b3b6541a7
Initial project setup including the main agent script, configuration files, profile JSON state, systemd service unit, startup script, deployment documentation, and Python dependencies. Includes .env, venv/, and __pycache__/ which should likely be excluded via .gitignore to avoid committing secrets and artifacts.
187 lines
8.5 KiB
Python
187 lines
8.5 KiB
Python
"""RootModel class and type definitions."""
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from __future__ import annotations as _annotations
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from collections.abc import Callable
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from copy import copy, deepcopy
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from typing import TYPE_CHECKING, Any, Generic, Literal, TypeVar
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from pydantic_core import PydanticUndefined
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from typing_extensions import Self, dataclass_transform
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from . import PydanticUserError
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from ._internal import _model_construction, _repr
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from .main import BaseModel, _object_setattr
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if TYPE_CHECKING:
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from .fields import Field as PydanticModelField
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from .fields import PrivateAttr as PydanticModelPrivateAttr
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from .main import IncEx
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# dataclass_transform could be applied to RootModel directly, but `ModelMetaclass`'s dataclass_transform
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# takes priority (at least with pyright). We trick type checkers into thinking we apply dataclass_transform
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# on a new metaclass.
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@dataclass_transform(kw_only_default=False, field_specifiers=(PydanticModelField, PydanticModelPrivateAttr))
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class _RootModelMetaclass(_model_construction.ModelMetaclass): ...
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else:
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_RootModelMetaclass = _model_construction.ModelMetaclass
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__all__ = ('RootModel',)
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RootModelRootType = TypeVar('RootModelRootType')
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class RootModel(BaseModel, Generic[RootModelRootType], metaclass=_RootModelMetaclass):
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"""!!! abstract "Usage Documentation"
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[`RootModel` and Custom Root Types](../concepts/models.md#rootmodel-and-custom-root-types)
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A Pydantic `BaseModel` for the root object of the model.
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Attributes:
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root: The root object of the model.
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__pydantic_root_model__: Whether the model is a RootModel.
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__pydantic_private__: Private fields in the model.
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__pydantic_extra__: Extra fields in the model.
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"""
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__pydantic_root_model__ = True
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__pydantic_private__ = None
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__pydantic_extra__ = None
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root: RootModelRootType
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def __init_subclass__(cls, **kwargs):
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extra = cls.model_config.get('extra')
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if extra is not None:
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raise PydanticUserError(
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"`RootModel` does not support setting `model_config['extra']`", code='root-model-extra'
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)
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super().__init_subclass__(**kwargs)
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def __init__(self, /, root: RootModelRootType = PydanticUndefined, **data) -> None: # type: ignore
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__tracebackhide__ = True
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if data:
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if root is not PydanticUndefined:
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raise ValueError(
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'"RootModel.__init__" accepts either a single positional argument or arbitrary keyword arguments'
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)
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root = data # type: ignore
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self.__pydantic_validator__.validate_python(root, self_instance=self)
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__init__.__pydantic_base_init__ = True # pyright: ignore[reportFunctionMemberAccess]
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@classmethod
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def model_construct(cls, root: RootModelRootType, _fields_set: set[str] | None = None) -> Self: # type: ignore
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"""Create a new model using the provided root object and update fields set.
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Args:
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root: The root object of the model.
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_fields_set: The set of fields to be updated.
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Returns:
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The new model.
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Raises:
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NotImplemented: If the model is not a subclass of `RootModel`.
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"""
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return super().model_construct(root=root, _fields_set=_fields_set)
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def __getstate__(self) -> dict[Any, Any]:
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return {
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'__dict__': self.__dict__,
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'__pydantic_fields_set__': self.__pydantic_fields_set__,
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}
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def __setstate__(self, state: dict[Any, Any]) -> None:
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_object_setattr(self, '__pydantic_fields_set__', state['__pydantic_fields_set__'])
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_object_setattr(self, '__dict__', state['__dict__'])
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def __copy__(self) -> Self:
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"""Returns a shallow copy of the model."""
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cls = type(self)
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m = cls.__new__(cls)
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new_dict = copy(self.__dict__)
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new_dict['root'] = copy(self.__dict__['root']) # pyright: ignore[reportIndexIssue] (https://github.com/microsoft/pyright/issues/11548)
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_object_setattr(m, '__dict__', new_dict)
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_object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))
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return m
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def __deepcopy__(self, memo: dict[int, Any] | None = None) -> Self:
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"""Returns a deep copy of the model."""
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cls = type(self)
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m = cls.__new__(cls)
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_object_setattr(m, '__dict__', deepcopy(self.__dict__, memo=memo))
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# This next line doesn't need a deepcopy because __pydantic_fields_set__ is a set[str],
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# and attempting a deepcopy would be marginally slower.
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_object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__))
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return m
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if TYPE_CHECKING:
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# This `model_dump()` definition is only provided to get a more accurate return type for type checkers
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# (no override is actually necessary at runtime). Generally, the return type will be `RootModelRootType`,
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# assuming that `RootModelRootType` is not a `BaseModel` subclass. If `RootModelRootType` is a `BaseModel`
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# subclass, then the return type will likely be `dict[str, Any]`, as `model_dump()` calls are recursive.
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# The return type could even be something different, in the case of a custom serializer. Thus, `Any` is
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# used here to catch all of these cases.
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def model_dump( # type: ignore
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self,
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*,
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mode: Literal['json', 'python'] | str = 'python',
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include: IncEx | None = None,
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exclude: IncEx | None = None,
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context: Any | None = None,
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by_alias: bool | 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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exclude_computed_fields: bool = False,
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round_trip: bool = False,
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warnings: bool | Literal['none', 'warn', 'error'] = True,
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fallback: Callable[[Any], Any] | None = None,
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serialize_as_any: bool = False,
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polymorphic_serialization: bool | None = None,
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) -> Any:
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"""!!! abstract "Usage Documentation"
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[`model_dump`](../concepts/serialization.md#python-mode)
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Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
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Args:
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mode: The mode in which `to_python` should run.
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If mode is 'json', the output will only contain JSON serializable types.
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If mode is 'python', the output may contain non-JSON-serializable Python objects.
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include: A set of fields to include in the output.
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exclude: A set of fields to exclude from the output.
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context: Additional context to pass to the serializer.
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by_alias: Whether to use the field's alias in the dictionary key if defined.
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exclude_unset: Whether to exclude fields that have not been explicitly set.
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exclude_defaults: Whether to exclude fields that are set to their default value.
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exclude_none: Whether to exclude fields that have a value of `None`.
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exclude_computed_fields: Whether to exclude computed fields.
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While this can be useful for round-tripping, it is usually recommended to use the dedicated
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`round_trip` parameter instead.
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round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T].
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warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors,
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"error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError].
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fallback: A function to call when an unknown value is encountered. If not provided,
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a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised.
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serialize_as_any: Whether to serialize fields with duck-typing serialization behavior.
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polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call.
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Returns:
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A dictionary representation of the model.
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"""
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...
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def __eq__(self, other: Any) -> bool:
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if not isinstance(other, RootModel):
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return NotImplemented
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return self.__pydantic_fields__['root'].annotation == other.__pydantic_fields__[
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'root'
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].annotation and super().__eq__(other)
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def __repr_args__(self) -> _repr.ReprArgs:
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yield 'root', self.root
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