antimatter.client.models.json_patch_request_tst_value

Antimatter Public API

Interact with the Antimatter Cloud API

The version of the OpenAPI document: 1.1.3 Contact: support@antimatter.io Generated by OpenAPI Generator (https://openapi-generator.tech)

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Module Contents

Classes

JSONPatchRequestTstValue

The value to test.

Attributes

JSONPATCHREQUESTTSTVALUE_ONE_OF_SCHEMAS

antimatter.client.models.json_patch_request_tst_value.JSONPATCHREQUESTTSTVALUE_ONE_OF_SCHEMAS = ['bool', 'float', 'str']

class antimatter.client.models.json_patch_request_tst_value.JSONPatchRequestTstValue(*args, **kwargs)

Bases: pydantic.BaseModel

The value to test.

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.

oneof_schema_1_validator: pydantic.StrictStr | None

oneof_schema_2_validator: pydantic.StrictFloat | pydantic.StrictInt | None

oneof_schema_3_validator: pydantic.StrictBool | None

actual_instance: bool | float | str | None

one_of_schemas: List[str]

model_config

actual_instance_must_validate_oneof(v)

classmethod from_dict(obj: dict) → Self

classmethod from_json(json_str: str) → Self

Returns the object represented by the json string

to_json() → str

Returns the JSON representation of the actual instance

to_dict() → Dict

Returns the dict representation of the actual instance

to_str() → str

Returns the string representation of the actual instance

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

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

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: typing_extensions.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.

Args:

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.

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. 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.

classmethod model_json_schema(by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE, schema_generator: type[pydantic.json_schema.GenerateJsonSchema] = GenerateJsonSchema, mode: pydantic.json_schema.JsonSchemaMode = '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], Ellipsis]) → 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(__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 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.

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.

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.

Args:

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.

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]

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 parse_obj(obj: Any) → Model

classmethod parse_raw(b: str | bytes, *, content_type: str | None = None, encoding: str = 'utf8', proto: pydantic.deprecated.parse.Protocol | None = None, allow_pickle: bool = False) → Model

classmethod parse_file(path: str | pathlib.Path, *, content_type: str | None = None, encoding: str = 'utf8', proto: pydantic.deprecated.parse.Protocol | None = None, allow_pickle: bool = False) → Model

classmethod from_orm(obj: Any) → Model

classmethod construct(_fields_set: set[str] | None = None, **values: Any) → Model

copy(*, include: pydantic._internal._utils.AbstractSetIntStr | pydantic._internal._utils.MappingIntStrAny | None = None, exclude: pydantic._internal._utils.AbstractSetIntStr | pydantic._internal._utils.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) `

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.

classmethod schema(by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE) → Dict[str, Any]

classmethod schema_json(*, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE, **dumps_kwargs: Any) → str

classmethod validate(value: Any) → Model

classmethod update_forward_refs(**localns: Any) → None