rberga06.utils.plugin.system.System#

class rberga06.utils.plugin.system.System(*, name: str, version: Version, package: str, Features: type[_F], path: list[Path] = None, plugins: dict[str, Plugin] = None, info_file: str = '.plugin.yml')#

Bases: BaseModel, Generic[_F]

A plugin system.

__init__(**data: Any) → None#

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

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

__init__ uses __pydantic_self__ instead of the more common self for the first arg to allow self as a field name.

Methods

`__init__`(**data)	Create a new model by parsing and validating input data from keyword arguments.
`compat_ensure`(obj, /)	Make sure the given plugin is compatible with self.
`compat_eval`(spec, /)	Evaluate compatibility with the given spec.
`construct`([_fields_set])
`copy`(*[, include, exclude, update, deep])	Returns a copy of the model.
`dict`(*[, include, exclude, by_alias, ...])
`discover`()	Discover compatible plugins at self.path (and register them).
`discover_all`()	Run and exhaust self.discover() and return self.
`extend_path`(*paths)	Extend self.path with the given paths.
`extend_path_pkg`(ns)	Extend self.path with the given (namespace?) package.
`from_orm`(obj)
`json`(*[, include, exclude, by_alias, ...])
`load`(plugin)	Load the given plugin (in-place).
`model_construct`([_fields_set])	Creates a new instance of the Model class with validated data.
`model_copy`(*[, update, deep])	Returns a copy of the model.
`model_dump`(*[, mode, include, exclude, ...])	Usage docs: https://docs.pydantic.dev/2.2/usage/serialization/#modelmodel_dump
`model_dump_json`(*[, indent, include, ...])	Usage docs: https://docs.pydantic.dev/2.2/usage/serialization/#modelmodel_dump_json
`model_json_schema`([by_alias, ref_template, ...])	Generates a JSON schema for a model class.
`model_parametrized_name`(params)	Compute the class name for parametrizations of generic classes.
`model_post_init`(_BaseModel__context)	Override this method to perform additional initialization after __init__ and model_construct.
`model_rebuild`(*[, force, raise_errors, ...])	Try to rebuild the pydantic-core schema for the model.
`model_validate`(obj, *[, strict, ...])	Validate a pydantic model instance.
`model_validate_json`(json_data, *[, strict, ...])	Validate the given JSON data against the Pydantic model.
`parse_file`(path, *[, content_type, ...])
`parse_obj`(obj)
`parse_raw`(b, *[, content_type, encoding, ...])
`register`(plugin)	Register the given plugin.
`schema`([by_alias, ref_template])
`schema_json`(*[, by_alias, ref_template])
`update_forward_refs`(**localns)
`validate`(value)

Attributes

`model_computed_fields`	Get the computed fields of this model instance.
`model_config`	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
`model_extra`	Get extra fields set during validation.
`model_fields`	Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].
`model_fields_set`	Returns the set of fields that have been set on this model instance.
`name`
`version`
`package`
`Features`
`path`
`plugins`
`info_file`

compat_ensure(obj: Plugin | Spec, /) → Plugin | Spec#: Make sure the given plugin is compatible with self.

compat_eval(spec: str, /) → bool#: Evaluate compatibility with the given spec.

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) `

Args:

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.

discover() → Iterator[Plugin]#: Discover compatible plugins at self.path (and register them).

discover_all() → Self#: Run and exhaust self.discover() and return self.

extend_path(*paths: Path | str) → Self#: Extend self.path with the given paths.

extend_path_pkg(ns: module | str) → Self#: Extend self.path with the given (namespace?) package.

load(plugin: Plugin) → Plugin#: Load the given plugin (in-place).

property model_computed_fields: dict[str, ComputedFieldInfo]#

Get the computed fields of this model instance.

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

model_config: ClassVar[ConfigDict] = {}#: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

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

Args:: _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#

Returns a copy of the model.

Args:

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/2.2/usage/serialization/#modelmodel_dump

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

Args:

mode: The mode in which to_python should run.: If mode is ‘json’, the dictionary will only contain JSON serializable types. If mode is ‘python’, the dictionary may contain any 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 are unset or None from the output. exclude_defaults: Whether to exclude fields that are set to their default value from the output. exclude_none: Whether to exclude fields that have a value of None from the output. round_trip: Whether to enable serialization and deserialization round-trip support. 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/2.2/usage/serialization/#modelmodel_dump_json

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

Args:: 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. Can take either a string or set of strings. exclude: Field(s) to exclude from the JSON output. Can take either a string or set of strings. 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 have the default value. exclude_none: Whether to exclude fields that have a value of None. round_trip: Whether to use serialization/deserialization between JSON and class instance. warnings: Whether to show any warnings that occurred during serialization.
Returns:: A JSON string representation of the model.

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”.

model_fields: ClassVar[dict[str, FieldInfo]] = {'Features': FieldInfo(annotation=type[~_F], required=True), 'info_file': FieldInfo(annotation=str, required=False, default='.plugin.yml'), 'name': FieldInfo(annotation=str, required=True), 'package': FieldInfo(annotation=str, required=True), 'path': FieldInfo(annotation=list[Path], required=False, default_factory=list), 'plugins': FieldInfo(annotation=dict[str, Plugin], required=False, default_factory=dict), 'version': FieldInfo(annotation=Version, required=True)}#

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str]#

Returns the set of fields that have been 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.

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.

Args:

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.

Args:

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(_BaseModel__context: Any) → None#: Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

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.

Args:: 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.

Args:: obj: The object to validate. strict: Whether to raise an exception on invalid fields. 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#

Validate the given JSON data against the Pydantic model.

Args:: 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.

register(plugin: Plugin) → Plugin#: Register the given plugin.