config_wrangler.config_templates.password_defaults module

pydantic model config_wrangler.config_templates.password_defaults.PasswordDefaults[source]

Bases: ConfigHierarchy

Config:

  • validate_default: bool = True

  • validate_assignment: bool = True

  • validate_credentials: bool = True

Fields:
field keepass: KeepassConfig | None = None

If the password_source is KEEPASS, then load a sub-section with the config_wrangler.config_templates.keepass_config.KeepassConfig) settings

field keepass_config: str = 'keepass'

If the password_source is KEEPASS, then which root level config item contains the settings for Keepass (must be an instance of config_wrangler.config_templates.keepass_config.KeepassConfig)

field password_source: PasswordSource | None = None
__init__(**data: Any) → None

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

Uses something other than self the first arg to allow “self” as a settable attribute

add_child(name: str, child_object: ConfigHierarchy)

Set this configuration as a child in the hierarchy of another config. For any programmatically created config objects this is required so that the new object ‘knows’ where it lives in the hierarchy – most importantly so that it can find the hierarchies root object.

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Model
copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: Dict[str, Any] | None = None, deep: bool = False) → Model

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

  • include – Optional set or mapping specifying which fields to include in the copied model.

  • exclude – Optional set or mapping specifying which fields to exclude in the copied model.

  • update – Optional dictionary of field-value pairs to override field values in the copied model.

  • deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include: IncEx = None, exclude: IncEx = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → Dict[str, Any]
classmethod from_orm(obj: Any) → Model
full_item_name(item_name: str = None, delimiter: str = ' -> ')

The fully qualified name of this config item in the config hierarchy.

get(section, item, fallback=Ellipsis)

Used as a drop in replacement for ConfigParser.get() with dynamic config field names (using a string variable for the section and item names instead of python code attribute access)

Warning

With this method Python code checkers (linters) will not warn about invalid config items. You can end up with runtime AttributeError errors.

get_copy(copied_by: str = 'get_copy') → ConfigHierarchy

Copy this configuration. Useful when you need to programmatically modify a configuration without modifying the original base configuration.

get_list(section, item, fallback=Ellipsis) → list

Used as a drop in replacement for ConfigParser.get() + list parsing with dynamic config field names (using a string variable for the section and item names instead of python code attribute access) that is then parsed as a list.

Warning

With this method Python code checkers (linters) will not warn about invalid config items. You can end up with runtime AttributeError errors.

getboolean(section, item, fallback=Ellipsis) → bool

Used as a drop in replacement for ConfigParser.getboolean() with dynamic config field names (using a string variable for the section and item names instead of python code attribute access)

Warning

With this method Python code checkers (linters) will not warn about invalid config items. You can end up with runtime AttributeError errors.

json(*, include: IncEx = None, exclude: IncEx = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = PydanticUndefined, models_as_dict: bool = PydanticUndefined, **dumps_kwargs: Any) → str
classmethod model_construct(_fields_set: set[str] | None = None, **values: Any) → Model

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

Parameters:

  • _fields_set – The set of field names accepted for the Model instance.

  • values – Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

model_copy(*, update: dict[str, Any] | None = None, deep: bool = False) → Model

Usage docs: https://docs.pydantic.dev/2.6/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

  • update – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

  • deep – Set to True to make a deep copy of the model.

Returns:

New model instance.

model_dump(*, mode: Literal['json', 'python'] | str = 'python', include: IncEx = None, exclude: IncEx = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool = True) → dict[str, Any]

Usage docs: https://docs.pydantic.dev/2.6/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

  • mode – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

  • include – A list of fields to include in the output.

  • exclude – A list of fields to exclude from the output.

  • by_alias – Whether to use the field’s alias in the dictionary key if defined.

  • exclude_unset – Whether to exclude fields that have not been explicitly set.

  • exclude_defaults – Whether to exclude fields that are set to their default value.

  • exclude_none – Whether to exclude fields that have a value of None.

  • round_trip – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

  • warnings – Whether to log warnings when invalid fields are encountered.

Returns:

A dictionary representation of the model.

model_dump_json(*, indent: int | None = None, include: IncEx = None, exclude: IncEx = None, by_alias: bool = False, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, round_trip: bool = False, warnings: bool = True) → str

Usage docs: https://docs.pydantic.dev/2.6/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

  • indent – Indentation to use in the JSON output. If None is passed, the output will be compact.

  • include – Field(s) to include in the JSON output.

  • exclude – Field(s) to exclude from the JSON output.

  • by_alias – Whether to serialize using field aliases.

  • exclude_unset – Whether to exclude fields that have not been explicitly set.

  • exclude_defaults – Whether to exclude fields that are set to their default value.

  • exclude_none – Whether to exclude fields that have a value of None.

  • round_trip – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

  • warnings – Whether to log warnings when invalid fields are encountered.

Returns:

A JSON string representation of the model.

model_dump_non_private(*, mode: Literal['json', 'python'] | str = 'python', exclude: Set[str] = None) → dict[str, Any]
classmethod model_json_schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}', schema_generator: type[~pydantic.json_schema.GenerateJsonSchema] = <class 'pydantic.json_schema.GenerateJsonSchema'>, mode: ~typing.Literal['validation', 'serialization'] = 'validation') → dict[str, Any]

Generates a JSON schema for a model class.

Parameters:

  • by_alias – Whether to use attribute aliases or not.

  • ref_template – The reference template.

  • schema_generator – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

  • mode – The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params: tuple[type[Any], ...]) → str

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(__context: Any) → None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that’s what pydantic-core passes when calling it.

Parameters:

  • self – The BaseModel instance.

  • __context – The context.

classmethod model_rebuild(*, force: bool = False, raise_errors: bool = True, _parent_namespace_depth: int = 2, _types_namespace: dict[str, Any] | None = None) → bool | None

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

  • force – Whether to force the rebuilding of the model schema, defaults to False.

  • raise_errors – Whether to raise errors, defaults to True.

  • _parent_namespace_depth – The depth level of the parent namespace, defaults to 2.

  • _types_namespace – The types namespace, defaults to None.

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj: Any, *, strict: bool | None = None, from_attributes: bool | None = None, context: dict[str, Any] | None = None) → Model

Validate a pydantic model instance.

Parameters:

  • obj – The object to validate.

  • strict – Whether to enforce types strictly.

  • from_attributes – Whether to extract data from object attributes.

  • context – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

classmethod model_validate_json(json_data: str | bytes | bytearray, *, strict: bool | None = None, context: dict[str, Any] | None = None) → Model

Usage docs: https://docs.pydantic.dev/2.6/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

  • json_data – The JSON data to validate.

  • strict – Whether to enforce types strictly.

  • context – Extra variables to pass to the validator.

Returns:

The validated Pydantic model.

Raises:

ValueError – If json_data is not a JSON string.

classmethod model_validate_strings(obj: Any, *, strict: bool | None = None, context: dict[str, Any] | None = None) → Model

Validate the given object contains string data against the Pydantic model.

Parameters:

  • obj – The object contains string data to validate.

  • strict – Whether to enforce types strictly.

  • context – Extra variables to pass to the validator.

Returns:

The validated Pydantic model.

classmethod parse_file(path: str | Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Model
classmethod parse_obj(obj: Any) → Model
classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: DeprecatedParseProtocol | None = None, allow_pickle: bool = False) → Model
classmethod schema(by_alias: bool = True, ref_template: str = '#/$defs/{model}') → Dict[str, Any]
classmethod schema_json(*, by_alias: bool = True, ref_template: str = '#/$defs/{model}', **dumps_kwargs: Any) → str
set_as_child(name: str, other_config_item: ConfigHierarchy)
static translate_config_data(config_data: MutableMapping)

Children classes can provide translation logic to allow older config files to be used with newer config class definitions.

classmethod update_forward_refs(**localns: Any) → None
classmethod validate(value: Any) → Model
model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.