config_wrangler.config_types.dynamically_referenced module

config_wrangler.config_types.dynamically_referenced.DynamicField(default: Any = PydanticUndefined, *, default_factory: Callable[[], Any] | None = PydanticUndefined, alias: str | None = PydanticUndefined, alias_priority: int | None = PydanticUndefined, validation_alias: str | AliasPath | AliasChoices | None = PydanticUndefined, serialization_alias: str | None = PydanticUndefined, title: str | None = PydanticUndefined, description: str | None = PydanticUndefined, examples: list[Any] | None = PydanticUndefined, exclude: bool | None = PydanticUndefined, include: bool | None = PydanticUndefined, discriminator: str | None = PydanticUndefined, json_schema_extra: dict[str, Any] | Callable[[dict[str, Any]], None] | None = PydanticUndefined, frozen: bool | None = PydanticUndefined, validate_default: bool | None = PydanticUndefined, repr: bool = PydanticUndefined, init_var: bool | None = PydanticUndefined, kw_only: bool | None = PydanticUndefined, pattern: str | None = PydanticUndefined, strict: bool | None = PydanticUndefined, gt: float | None = PydanticUndefined, ge: float | None = PydanticUndefined, lt: float | None = PydanticUndefined, le: float | None = PydanticUndefined, multiple_of: float | None = PydanticUndefined, allow_inf_nan: bool | None = PydanticUndefined, max_digits: int | None = PydanticUndefined, decimal_places: int | None = PydanticUndefined, min_length: int | None = PydanticUndefined, max_length: int | None = PydanticUndefined, delimiter: str = ',') → Any

Create a field for a list of objects, plus other Pydantic Field configuration options.

Pydantic standard docs:

Used to provide extra information about a field, either for the model schema or complex validation. Some arguments apply only to number fields (int, float, Decimal) and some apply only to str.

Parameters:

• default – Default value if the field is not set.

• default_factory – A callable to generate the default value, such as utcnow().

• alias – An alternative name for the attribute.

• alias_priority – Priority of the alias. This affects whether an alias generator is used.

• validation_alias – ‘Whitelist’ validation step. The field will be the single one allowed by the alias or set of aliases defined.

• serialization_alias – ‘Blacklist’ validation step. The vanilla field will be the single one of the alias’ or set of aliases’ fields and all the other fields will be ignored at serialization time.

• title – Human-readable title.

• description – Human-readable description.

• examples – Example values for this field.

• exclude – Whether to exclude the field from the model schema.

• include – Whether to include the field in the model schema.

• discriminator – Field name for discriminating the type in a tagged union.

• json_schema_extra – Any additional JSON schema data for the schema property.

• frozen – Whether the field is frozen.

• validate_default – Run validation that isn’t only checking existence of defaults. True by default.

• repr – A boolean indicating whether to include the field in the __repr__ output.

• init_var – Whether the field should be included in the constructor of the dataclass.

• kw_only – Whether the field should be a keyword-only argument in the constructor of the dataclass.

• strict – If True, strict validation is applied to the field. See [Strict Mode](../usage/strict_mode.md) for details.

• gt – Greater than. If set, value must be greater than this. Only applicable to numbers.

• ge – Greater than or equal. If set, value must be greater than or equal to this. Only applicable to numbers.

• lt – Less than. If set, value must be less than this. Only applicable to numbers.

• le – Less than or equal. If set, value must be less than or equal to this. Only applicable to numbers.

• multiple_of – Value must be a multiple of this. Only applicable to numbers.

• min_length – Minimum length for strings.

• max_length – Maximum length for strings.

• pattern – Pattern for strings.

• allow_inf_nan – Allow inf, -inf, nan. Only applicable to numbers.

• max_digits – Maximum number of allow digits for strings.

• decimal_places – Maximum number of decimal places allowed for numbers.

• delimiter – delimiter to use when parsing the input value

Returns:

A new [FieldInfo][pydantic.fields.FieldInfo], the return annotation is Any so Field can be used on

type annotated fields without causing a typing error.

class config_wrangler.config_types.dynamically_referenced.DynamicFieldInfo(delimiter=',', **kwargs)

Bases: DelimitedListFieldInfo

__init__(delimiter=',', **kwargs) → None

This class should generally not be initialized directly; instead, use the pydantic.fields.Field function or one of the constructor classmethods.

See the signature of pydantic.fields.Field for more details about the expected arguments.

alias: str | None

alias_priority: int | None

annotation: type[Any] | None

apply_typevars_map(typevars_map: dict[Any, Any] | None, types_namespace: dict[str, Any] | None) → None

Apply a typevars_map to the annotation.

This method is used when analyzing parametrized generic types to replace typevars with their concrete types.

This method applies the typevars_map to the annotation in place.

Parameters:

• typevars_map – A dictionary mapping type variables to their concrete types.

• types_namespace (dict | None) – A dictionary containing related types to the annotated type.

See also

pydantic._internal._generics.replace_types is used for replacing the typevars with

their concrete types.

default: Any

default_factory: Callable[[], Any] | None

delimiter: str

The delimiter to use when parsing the value into a list. (DelimitedListFieldInfo specific)

description: str | None

discriminator: str | types.Discriminator | None

examples: list[Any] | None

exclude: bool | None

static from_annotated_attribute(annotation: type[Any], default: Any) → FieldInfo

Create FieldInfo from an annotation with a default value.

This is used in cases like the following:

```python import annotated_types from typing_extensions import Annotated

import pydantic

class MyModel(pydantic.BaseModel):

foo: int = 4 # <– like this bar: Annotated[int, annotated_types.Gt(4)] = 4 # <– or this spam: Annotated[int, pydantic.Field(gt=4)] = 4 # <– or this

```

Parameters:

• annotation – The type annotation of the field.

• default – The default value of the field.

Returns:

A field object with the passed values.

static from_annotation(annotation: type[Any]) → FieldInfo

Creates a FieldInfo instance from a bare annotation.

This function is used internally to create a FieldInfo from a bare annotation like this:

```python import pydantic

class MyModel(pydantic.BaseModel):

foo: int # <– like this

```

We also account for the case where the annotation can be an instance of Annotated and where one of the (not first) arguments in Annotated is an instance of FieldInfo, e.g.:

```python import annotated_types from typing_extensions import Annotated

import pydantic

class MyModel(pydantic.BaseModel):

foo: Annotated[int, annotated_types.Gt(42)] bar: Annotated[int, pydantic.Field(gt=42)]

```

Parameters:

annotation – An annotation object.

Returns:

An instance of the field metadata.

static from_field(default: Any = PydanticUndefined, **kwargs: Unpack[_FromFieldInfoInputs]) → DynamicFieldInfo

Create a new FieldInfo object with the Field function.

Parameters:

• default – The default value for the field. Defaults to Undefined.

• **kwargs – Additional arguments dictionary.

Raises:

TypeError – If ‘annotation’ is passed as a keyword argument.

Returns:

A new FieldInfo object with the given parameters.

Example

This is how you can create a field with default value like this:

```python import pydantic

class MyModel(pydantic.BaseModel):

foo: int = pydantic.Field(4)

```

frozen: bool | None

get_default(*, call_default_factory: bool = False) → Any

Get the default value.

We expose an option for whether to call the default_factory (if present), as calling it may result in side effects that we want to avoid. However, there are times when it really should be called (namely, when instantiating a model via model_construct).

Parameters:

call_default_factory – Whether to call the default_factory or not. Defaults to False.

Returns:

The default value, calling the default factory if requested or None if not set.

init: bool | None

init_var: bool | None

is_required() → bool

Check if the field is required (i.e., does not have a default value or factory).

Returns:

True if the field is required, False otherwise.

json_schema_extra: JsonDict | Callable[[JsonDict], None] | None

kw_only: bool | None

static merge_field_infos(*field_infos: FieldInfo, **overrides: Any) → FieldInfo

Merge FieldInfo instances keeping only explicitly set attributes.

Later FieldInfo instances override earlier ones.

Returns:

A merged FieldInfo instance.

Return type:

FieldInfo

metadata: list[Any]

metadata_lookup: ClassVar[dict[str, Callable[[Any], Any] | None]] = {'allow_inf_nan': None, 'decimal_places': None, 'ge': <class 'annotated_types.Ge'>, 'gt': <class 'annotated_types.Gt'>, 'le': <class 'annotated_types.Le'>, 'lt': <class 'annotated_types.Lt'>, 'max_digits': None, 'max_length': <class 'annotated_types.MaxLen'>, 'min_length': <class 'annotated_types.MinLen'>, 'multiple_of': <class 'annotated_types.MultipleOf'>, 'pattern': None, 'strict': <class 'pydantic.types.Strict'>, 'union_mode': None}

rebuild_annotation() → Any

Attempts to rebuild the original annotation for use in function signatures.

If metadata is present, it adds it to the original annotation using Annotated. Otherwise, it returns the original annotation as-is.

Note that because the metadata has been flattened, the original annotation may not be reconstructed exactly as originally provided, e.g. if the original type had unrecognized annotations, or was annotated with a call to pydantic.Field.

Returns:

The rebuilt annotation.

repr: bool

serialization_alias: str | None

title: str | None

validate_default: bool | None

validation_alias: str | AliasPath | AliasChoices | None

pydantic model config_wrangler.config_types.dynamically_referenced.DynamicallyReferenced

Bases: ConfigHierarchy

Represents a reference to a statically defined section of the config. The data type of the section can be any subclass of ConfigHierarchy. The validator will check that the reference exists.

Config:

• validate_default: bool = True

• validate_assignment: bool = True

• validate_credentials: bool = True

Fields:

• ref (str)

Validators:

• _validate_phase_1 » ref

field ref: str [Required]

Validated by:

• _validate_phase_1

__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.

get_referenced() → ConfigHierarchy

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.

pydantic model config_wrangler.config_types.dynamically_referenced.ListDynamicallyReferenced

Bases: ConfigHierarchy

Config:

• validate_default: bool = True

• validate_assignment: bool = True

• validate_credentials: bool = True

Fields:

• refs (List[DynamicallyReferenced])

field refs: List[DynamicallyReferenced] [Required]

__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.