antimatter.client.models - Antimatter Python Client documentation

class antimatter.client.models.APIKeyDomainIdentityProviderDetails(/, **data: Any)#

Bases: pydantic.BaseModel

Detailed information about an API key identity provider

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.

type: pydantic.StrictStr#

model_config#

type_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of APIKeyDomainIdentityProviderDetails from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of APIKeyDomainIdentityProviderDetails from a dict

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

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#

class antimatter.client.models.AWSServiceAccountKeyInfo(/, **data: Any)#

Bases: pydantic.BaseModel

The AWS service account information and details required to use the provided AWS hosted encryption keys for cryptographic operations.

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.

access_key_id: pydantic.StrictStr#

secret_access_key: pydantic.StrictStr#

key_arn: pydantic.StrictStr#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of AWSServiceAccountKeyInfo from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of AWSServiceAccountKeyInfo from a dict

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

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#

class antimatter.client.models.AccessLogEntry(/, **data: Any)#

Bases: pydantic.BaseModel

An individual capsule data-plane log entry. If adding a new read log entry, the session should be omitted (the server will fill it in)

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.

id: typing_extensions.Annotated[str, Field(strict=True)]#

time: datetime.datetime#

domain: typing_extensions.Annotated[str, Field(strict=True)]#

capsule: typing_extensions.Annotated[str, Field(strict=True)]#

operation: pydantic.StrictStr#

session: typing_extensions.Annotated[str, Field(strict=True)]#

location: pydantic.StrictStr | None#

create_info: antimatter.client.models.access_log_entry_create_info.AccessLogEntryCreateInfo | None#

open_info: antimatter.client.models.access_log_entry_open_info.AccessLogEntryOpenInfo | None#

read_info: antimatter.client.models.access_log_entry_read_info.AccessLogEntryReadInfo | None#

model_config#

id_validate_regular_expression(value)#: Validates the regular expression

domain_validate_regular_expression(value)#: Validates the regular expression

capsule_validate_regular_expression(value)#: Validates the regular expression

operation_validate_enum(value)#: Validates the enum

session_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of AccessLogEntry from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of AccessLogEntry from a dict

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

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#

class antimatter.client.models.AccessLogEntryCreateInfo(/, **data: Any)#

Bases: pydantic.BaseModel

information available if the operation is of type “create”.

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.

write_context: typing_extensions.Annotated[str, Field(strict=True)]#

model_config#

write_context_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of AccessLogEntryCreateInfo from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of AccessLogEntryCreateInfo from a dict

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

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#

class antimatter.client.models.AccessLogEntryOpenInfo(/, **data: Any)#

Bases: pydantic.BaseModel

information available if the operation is of type “open”.

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.

read_context: typing_extensions.Annotated[str, Field(strict=True)]#

model_config#

read_context_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of AccessLogEntryOpenInfo from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of AccessLogEntryOpenInfo from a dict

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

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#

class antimatter.client.models.AccessLogEntryReadInfo(/, **data: Any)#

Bases: pydantic.BaseModel

information available if the operation is of type “read”. allowedTags are those that were allowed without transformation during the read. redactedTags are those that were redacted during the read. tokenizedTags are those that were tokenized during the read.

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.

parameters: Dict[str, pydantic.StrictStr]#

read_context: typing_extensions.Annotated[str, Field(strict=True)]#

allowed_tags: antimatter.client.models.tag_summary.TagSummary#

redacted_tags: antimatter.client.models.tag_summary.TagSummary#

tokenized_tags: antimatter.client.models.tag_summary.TagSummary#

returned_records: pydantic.StrictInt#

filtered_records: pydantic.StrictInt#

model_config#

read_context_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of AccessLogEntryReadInfo from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of AccessLogEntryReadInfo from a dict

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

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#

class antimatter.client.models.AccessLogResults(/, **data: Any)#

Bases: pydantic.BaseModel

The results for a query of the capsule access log

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.

results: List[antimatter.client.models.access_log_entry.AccessLogEntry]#

has_more: pydantic.StrictBool#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of AccessLogResults from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of AccessLogResults from a dict

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

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#

class antimatter.client.models.ActiveRootEncryptionKeyID(/, **data: Any)#

Bases: pydantic.BaseModel

The stored key ID to use as the active root encryption key.

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.

key_id: typing_extensions.Annotated[str, Field(strict=True)]#

model_config#

key_id_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of ActiveRootEncryptionKeyID from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of ActiveRootEncryptionKeyID from a dict

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

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#

class antimatter.client.models.AddCapsuleLogEntryRequest(/, **data: Any)#

Bases: pydantic.BaseModel

A request to add a capsule log entry

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.

open_token: typing_extensions.Annotated[str, Field(min_length=64, strict=True)]#

entry: antimatter.client.models.new_access_log_entry.NewAccessLogEntry#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of AddCapsuleLogEntryRequest from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of AddCapsuleLogEntryRequest from a dict

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

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#

class antimatter.client.models.AddReadContext(/, **data: Any)#

Bases: pydantic.BaseModel

A request to add read contexts

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.

summary: typing_extensions.Annotated[str, Field(strict=True, max_length=140)]#

description: typing_extensions.Annotated[str, Field(strict=True, max_length=4096)]#

disable_read_logging: pydantic.StrictBool | None#

key_cache_ttl: Optional[typing_extensions.Annotated[int, Field(strict=True, ge=0)]]#

required_hooks: List[antimatter.client.models.read_context_required_hook.ReadContextRequiredHook] | None#

read_parameters: List[antimatter.client.models.read_context_parameter.ReadContextParameter] | None#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of AddReadContext from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of AddReadContext from a dict

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

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#

class antimatter.client.models.AddWriteContext(/, **data: Any)#

Bases: pydantic.BaseModel

Information for adding/updating a write context

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.

summary: typing_extensions.Annotated[str, Field(strict=True, max_length=140)]#

description: typing_extensions.Annotated[str, Field(strict=True, max_length=4096)]#

config: antimatter.client.models.write_context_config_info.WriteContextConfigInfo#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of AddWriteContext from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of AddWriteContext from a dict

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

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#

class antimatter.client.models.AntimatterDelegatedAWSKeyInfo(/, **data: Any)#

Bases: pydantic.BaseModel

The details required to use an AWS KMS root encryption key that has been delegated to Antimatter’s AWS account. This will use Antimatter’s service account during set up of the AWS client.

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.

key_arn: pydantic.StrictStr#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of AntimatterDelegatedAWSKeyInfo from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of AntimatterDelegatedAWSKeyInfo from a dict

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

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#

class antimatter.client.models.AvailableDelegatedRootEncryptionKeyProvider(/, **data: Any)#

Bases: pydantic.BaseModel

AvailableDelegatedRootEncryptionKeyProvider

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.

name: pydantic.StrictStr#

short_name: pydantic.StrictStr#

description: pydantic.StrictStr#

account_details: pydantic.StrictStr#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of AvailableDelegatedRootEncryptionKeyProvider from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of AvailableDelegatedRootEncryptionKeyProvider from a dict

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

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#

class antimatter.client.models.AvailableRootEncryptionKeyProviders(/, **data: Any)#

Bases: pydantic.BaseModel

AvailableRootEncryptionKeyProviders

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.

providers: List[antimatter.client.models.available_root_encryption_key_providers_providers_inner.AvailableRootEncryptionKeyProvidersProvidersInner] | None#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of AvailableRootEncryptionKeyProviders from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of AvailableRootEncryptionKeyProviders from a dict

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

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#

class antimatter.client.models.AvailableRootEncryptionKeyProvidersProvidersInner(*args, **kwargs)#

Bases: pydantic.BaseModel

AvailableRootEncryptionKeyProvidersProvidersInner

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: antimatter.client.models.available_delegated_root_encryption_key_provider.AvailableDelegatedRootEncryptionKeyProvider | None#

oneof_schema_2_validator: antimatter.client.models.available_service_account_root_encryption_key_provider.AvailableServiceAccountRootEncryptionKeyProvider | None#

actual_instance: antimatter.client.models.available_delegated_root_encryption_key_provider.AvailableDelegatedRootEncryptionKeyProvider | antimatter.client.models.available_service_account_root_encryption_key_provider.AvailableServiceAccountRootEncryptionKeyProvider | 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

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#

class antimatter.client.models.AvailableServiceAccountRootEncryptionKeyProvider(/, **data: Any)#

Bases: pydantic.BaseModel

AvailableServiceAccountRootEncryptionKeyProvider

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.

name: pydantic.StrictStr#

short_name: pydantic.StrictStr#

description: pydantic.StrictStr#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of AvailableServiceAccountRootEncryptionKeyProvider from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of AvailableServiceAccountRootEncryptionKeyProvider from a dict

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

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#

class antimatter.client.models.Capability(/, **data: Any)#

Bases: pydantic.BaseModel

A capability is attached to authenticated domain identities by an identity provider, and confers additional permissions upon the identity. This is done by writing domain policy rules that reference the capability.

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.

name: typing_extensions.Annotated[str, Field(strict=True)]#

value: Optional[typing_extensions.Annotated[str, Field(strict=True, max_length=256)]]#

model_config#

name_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of Capability from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of Capability from a dict

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

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#

class antimatter.client.models.CapabilityDefinition(/, **data: Any)#

Bases: pydantic.BaseModel

A capability is attached to authenticated domain identities by an identity provider, and confers additional permissions upon the identity. This is done by writing domain policy rules that reference the capability.

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.

name: typing_extensions.Annotated[str, Field(strict=True)]#

unary: pydantic.StrictBool#

summary: typing_extensions.Annotated[str, Field(strict=True, max_length=140)]#

description: typing_extensions.Annotated[str, Field(strict=True, max_length=4096)]#

imported: pydantic.StrictBool#

source_domain_id: Optional[typing_extensions.Annotated[str, Field(strict=True)]]#

source_domain_name: pydantic.StrictStr | None#

model_config#

name_validate_regular_expression(value)#: Validates the regular expression

source_domain_id_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of CapabilityDefinition from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of CapabilityDefinition from a dict

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

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#

class antimatter.client.models.CapabilityDefinitionList(/, **data: Any)#

Bases: pydantic.BaseModel

A list of capability definitions

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.

capabilities: List[antimatter.client.models.capability_definition.CapabilityDefinition]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of CapabilityDefinitionList from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of CapabilityDefinitionList from a dict

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

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#

class antimatter.client.models.CapabilityList(/, **data: Any)#

Bases: pydantic.BaseModel

A list of capabilities

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.

capabilities: List[antimatter.client.models.capability.Capability]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of CapabilityList from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of CapabilityList from a dict

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

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#

class antimatter.client.models.CapabilityRule(/, **data: Any)#

Bases: pydantic.BaseModel

A rule that refers to a domain identity capability. These rules are ANDed together

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.

match_expressions: List[antimatter.client.models.capability_rule_match_expressions_inner.CapabilityRuleMatchExpressionsInner] | None#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of CapabilityRule from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of CapabilityRule from a dict

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

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#

class antimatter.client.models.CapabilityRuleMatchExpressionsInner(/, **data: Any)#

Bases: pydantic.BaseModel

CapabilityRuleMatchExpressionsInner

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.

name: typing_extensions.Annotated[str, Field(strict=True)]#

operator: pydantic.StrictStr#

values: List[pydantic.StrictStr]#

model_config#

name_validate_regular_expression(value)#: Validates the regular expression

operator_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of CapabilityRuleMatchExpressionsInner from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of CapabilityRuleMatchExpressionsInner from a dict

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

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#

class antimatter.client.models.CapsuleCreateResponse(/, **data: Any)#

Bases: pydantic.BaseModel

The response for the creation of a new capsule

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.

id: typing_extensions.Annotated[str, Field(strict=True)]#

data_key: pydantic.StrictBytes | pydantic.StrictStr#

encrypted_data_key: pydantic.StrictBytes | pydantic.StrictStr#

key_encryption_key_id: typing_extensions.Annotated[int, Field(strict=True, ge=0)]#

create_token: typing_extensions.Annotated[str, Field(min_length=64, strict=True)]#

write_context_configuration: antimatter.client.models.write_context_config_info.WriteContextConfigInfo#

model_config#

id_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of CapsuleCreateResponse from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of CapsuleCreateResponse from a dict

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

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#

class antimatter.client.models.CapsuleInfo(/, **data: Any)#

Bases: pydantic.BaseModel

A summary of the capsule

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.

id: typing_extensions.Annotated[str, Field(strict=True)]#

domain: typing_extensions.Annotated[str, Field(strict=True)]#

capsule_tags: List[antimatter.client.models.tag.Tag]#

span_tags: antimatter.client.models.tag_summary.TagSummary#

size: pydantic.StrictInt#

created: datetime.datetime#

page_key: pydantic.StrictStr | None#

model_config#

id_validate_regular_expression(value)#: Validates the regular expression

domain_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of CapsuleInfo from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of CapsuleInfo from a dict

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

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#

class antimatter.client.models.CapsuleList(/, **data: Any)#

Bases: pydantic.BaseModel

List of capsules

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.

results: List[antimatter.client.models.capsule_info.CapsuleInfo]#

has_more: pydantic.StrictBool#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of CapsuleList from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of CapsuleList from a dict

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

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#

class antimatter.client.models.CapsuleOpenRequest(/, **data: Any)#

Bases: pydantic.BaseModel

A request to open (decrypt) a capsule

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.

encrypted_dek: pydantic.StrictBytes | pydantic.StrictStr#

key_id: pydantic.StrictInt#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of CapsuleOpenRequest from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of CapsuleOpenRequest from a dict

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

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#

class antimatter.client.models.CapsuleOpenResponse(/, **data: Any)#

Bases: pydantic.BaseModel

Contains key material for a capsule

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.

decryption_key: pydantic.StrictBytes | pydantic.StrictStr#

read_context_configuration: antimatter.client.models.capsule_open_response_read_context_configuration.CapsuleOpenResponseReadContextConfiguration#

open_token: typing_extensions.Annotated[str, Field(min_length=64, strict=True)]#

capsule_tags: List[antimatter.client.models.tag.Tag]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of CapsuleOpenResponse from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of CapsuleOpenResponse from a dict

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

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#

class antimatter.client.models.CapsuleOpenResponseReadContextConfiguration(/, **data: Any)#

Bases: pydantic.BaseModel

the material required for enacting read context configuration (e.g. wasm stuff)

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.

disable_read_logging: pydantic.StrictBool | None#

key_cache_ttl: Optional[typing_extensions.Annotated[int, Field(strict=True, ge=0)]]#

policy_assembly: pydantic.StrictBytes | pydantic.StrictStr#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of CapsuleOpenResponseReadContextConfiguration from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of CapsuleOpenResponseReadContextConfiguration from a dict

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

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#

class antimatter.client.models.CapsuleSealRequest(/, **data: Any)#

Bases: pydantic.BaseModel

Information applied when sealing a capsule (marking it as complete)

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.

capsule_tags: List[antimatter.client.models.tag.Tag]#

span_tags: antimatter.client.models.tag_summary.TagSummary#

size: pydantic.StrictInt#

create_token: typing_extensions.Annotated[str, Field(min_length=64, strict=True)]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of CapsuleSealRequest from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of CapsuleSealRequest from a dict

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

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#

class antimatter.client.models.ConflictError(/, **data: Any)#

Bases: pydantic.BaseModel

Returned when attempting to delete a resource that is still in use by other resources

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.

resource_type: pydantic.StrictStr#

identifier: pydantic.StrictStr#

message: pydantic.StrictStr#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of ConflictError from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of ConflictError from a dict

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

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#

class antimatter.client.models.CreatePeerDomain(/, **data: Any)#

Bases: pydantic.BaseModel

Configuration options for creating a new subdomain.

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.

nicknames: Optional[List[typing_extensions.Annotated[str, Field(strict=True, max_length=128)]]]#

import_alias_for_parent: Optional[typing_extensions.Annotated[str, Field(strict=True)]]#

import_alias_for_child: typing_extensions.Annotated[str, Field(strict=True)]#

display_name_for_parent: Optional[typing_extensions.Annotated[str, Field(min_length=1, strict=True, max_length=40)]]#

display_name_for_child: typing_extensions.Annotated[str, Field(min_length=1, strict=True, max_length=40)]#

link_all: pydantic.StrictBool | None#

link_identity_providers: pydantic.StrictBool | None#

link_facts: pydantic.StrictBool | None#

link_read_contexts: pydantic.StrictBool | None#

link_write_contexts: pydantic.StrictBool | None#

link_capabilities: pydantic.StrictBool | None#

link_domain_policy: pydantic.StrictBool | None#

link_capsule_access_log: pydantic.StrictBool | None#

link_control_log: pydantic.StrictBool | None#

link_capsule_manifest: pydantic.StrictBool | None#

model_config#

import_alias_for_parent_validate_regular_expression(value)#: Validates the regular expression

import_alias_for_child_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of CreatePeerDomain from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of CreatePeerDomain from a dict

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

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#

class antimatter.client.models.DataTaggingHookInput(/, **data: Any)#

Bases: pydantic.BaseModel

A request to classify PII in a batch of records

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.

records: List[antimatter.client.models.data_tagging_hook_input_records_inner.DataTaggingHookInputRecordsInner]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DataTaggingHookInput from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DataTaggingHookInput from a dict

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

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#

class antimatter.client.models.DataTaggingHookInputRecordsInner(/, **data: Any)#

Bases: pydantic.BaseModel

DataTaggingHookInputRecordsInner

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.

elements: List[antimatter.client.models.data_tagging_hook_input_records_inner_elements_inner.DataTaggingHookInputRecordsInnerElementsInner]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DataTaggingHookInputRecordsInner from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DataTaggingHookInputRecordsInner from a dict

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

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#

class antimatter.client.models.DataTaggingHookInputRecordsInnerElementsInner(/, **data: Any)#

Bases: pydantic.BaseModel

DataTaggingHookInputRecordsInnerElementsInner

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.

content: pydantic.StrictStr#

path: pydantic.StrictStr#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DataTaggingHookInputRecordsInnerElementsInner from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DataTaggingHookInputRecordsInnerElementsInner from a dict

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

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#

class antimatter.client.models.DataTaggingHookResponse(/, **data: Any)#

Bases: pydantic.BaseModel

A response from invoking a data tagging hook

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.

version: typing_extensions.Annotated[str, Field(strict=True)]#

records: List[antimatter.client.models.data_tagging_hook_response_records_inner.DataTaggingHookResponseRecordsInner]#

model_config#

version_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DataTaggingHookResponse from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DataTaggingHookResponse from a dict

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

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#

class antimatter.client.models.DataTaggingHookResponseRecordsInner(/, **data: Any)#

Bases: pydantic.BaseModel

DataTaggingHookResponseRecordsInner

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.

elements: List[antimatter.client.models.tag_set.TagSet]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DataTaggingHookResponseRecordsInner from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DataTaggingHookResponseRecordsInner from a dict

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

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#

class antimatter.client.models.DeleteTags(/, **data: Any)#

Bases: pydantic.BaseModel

DeleteTags

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.

names: List[pydantic.StrictStr] | None#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DeleteTags from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DeleteTags from a dict

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

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#

class antimatter.client.models.Domain(/, **data: Any)#

Bases: pydantic.BaseModel

Information about a domain

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.

id: typing_extensions.Annotated[str, Field(strict=True)]#

model_config#

id_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of Domain from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of Domain from a dict

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

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#

class antimatter.client.models.DomainAddReadContextRule200Response(/, **data: Any)#

Bases: pydantic.BaseModel

DomainAddReadContextRule200Response

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.

id: Optional[typing_extensions.Annotated[str, Field(strict=True)]]#

model_config#

id_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainAddReadContextRule200Response from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainAddReadContextRule200Response from a dict

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

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#

class antimatter.client.models.DomainAuthenticate(/, **data: Any)#

Bases: pydantic.BaseModel

An object containing external credentials that can be transmuted into a domain identity token

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.

token: pydantic.StrictStr#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainAuthenticate from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainAuthenticate from a dict

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

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#

class antimatter.client.models.DomainAuthenticateResponse(/, **data: Any)#

Bases: pydantic.BaseModel

A domain identity token

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.

token: pydantic.StrictStr#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainAuthenticateResponse from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainAuthenticateResponse from a dict

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

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#

class antimatter.client.models.DomainContactIssueVerifyRequest(/, **data: Any)#

Bases: pydantic.BaseModel

Parameters to request new validation request

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.

admin_email: typing_extensions.Annotated[str, Field(min_length=6, strict=True)]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainContactIssueVerifyRequest from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainContactIssueVerifyRequest from a dict

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

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#

class antimatter.client.models.DomainControlLogEntry(/, **data: Any)#

Bases: pydantic.BaseModel

Results for a domain control log query

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.

domain: typing_extensions.Annotated[str, Field(strict=True)]#

id: typing_extensions.Annotated[str, Field(strict=True)]#

time: datetime.datetime#

session: typing_extensions.Annotated[str, Field(strict=True)]#

url: pydantic.StrictStr#

summary: typing_extensions.Annotated[str, Field(strict=True, max_length=100)]#

description: Dict[str, pydantic.StrictStr]#

model_config#

domain_validate_regular_expression(value)#: Validates the regular expression

id_validate_regular_expression(value)#: Validates the regular expression

session_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainControlLogEntry from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainControlLogEntry from a dict

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

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#

class antimatter.client.models.DomainControlLogResults(/, **data: Any)#

Bases: pydantic.BaseModel

The results for a query of the capsule access log

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.

results: List[antimatter.client.models.domain_control_log_entry.DomainControlLogEntry]#

has_more: pydantic.StrictBool#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainControlLogResults from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainControlLogResults from a dict

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

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#

class antimatter.client.models.DomainFactList(/, **data: Any)#

Bases: pydantic.BaseModel

A list of defined fact types in the domain

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.

fact_types: List[antimatter.client.models.fact_type_definition.FactTypeDefinition]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainFactList from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainFactList from a dict

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

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#

class antimatter.client.models.DomainHooksList(/, **data: Any)#

Bases: pydantic.BaseModel

A list of available hooks in this domain

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.

hooks: List[antimatter.client.models.domain_hooks_list_hooks_inner.DomainHooksListHooksInner] | None#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainHooksList from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainHooksList from a dict

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

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#

class antimatter.client.models.DomainHooksListHooksInner(/, **data: Any)#

Bases: pydantic.BaseModel

DomainHooksListHooksInner

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.

name: typing_extensions.Annotated[str, Field(strict=True)]#

url: pydantic.StrictStr#

version: pydantic.StrictStr#

summary: pydantic.StrictStr#

description: pydantic.StrictStr#

output_span_tags: List[pydantic.StrictStr]#

output_capsule_tags: List[pydantic.StrictStr]#

model_config#

name_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainHooksListHooksInner from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainHooksListHooksInner from a dict

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

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#

class antimatter.client.models.DomainIdentityAPIKeyPrincipalParams(/, **data: Any)#

Bases: pydantic.BaseModel

Details for an API key principal

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.

type: pydantic.StrictStr#

api_key_id: pydantic.StrictStr | None#

comment: pydantic.StrictStr | None#

model_config#

type_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainIdentityAPIKeyPrincipalParams from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainIdentityAPIKeyPrincipalParams from a dict

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

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#

class antimatter.client.models.DomainIdentityEmailPrincipalParams(/, **data: Any)#

Bases: pydantic.BaseModel

Details for an email principal

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.

type: pydantic.StrictStr#

email: pydantic.StrictStr#

model_config#

type_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainIdentityEmailPrincipalParams from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainIdentityEmailPrincipalParams from a dict

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

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#

class antimatter.client.models.DomainIdentityHostedDomainPrincipalParams(/, **data: Any)#

Bases: pydantic.BaseModel

Additional details for a hosted domain principal

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.

type: pydantic.StrictStr#

hosted_domain: pydantic.StrictStr#

model_config#

type_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainIdentityHostedDomainPrincipalParams from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainIdentityHostedDomainPrincipalParams from a dict

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

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#

class antimatter.client.models.DomainIdentityPrincipalDetails(*args, **kwargs)#

Bases: pydantic.BaseModel

DomainIdentityPrincipalDetails

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: antimatter.client.models.domain_identity_api_key_principal_params.DomainIdentityAPIKeyPrincipalParams | None#

oneof_schema_2_validator: antimatter.client.models.domain_identity_email_principal_params.DomainIdentityEmailPrincipalParams | None#

oneof_schema_3_validator: antimatter.client.models.domain_identity_hosted_domain_principal_params.DomainIdentityHostedDomainPrincipalParams | None#

actual_instance: antimatter.client.models.domain_identity_api_key_principal_params.DomainIdentityAPIKeyPrincipalParams | antimatter.client.models.domain_identity_email_principal_params.DomainIdentityEmailPrincipalParams | antimatter.client.models.domain_identity_hosted_domain_principal_params.DomainIdentityHostedDomainPrincipalParams | None#

one_of_schemas: List[str]#

model_config#

discriminator_value_class_map: Dict[str, str]#

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

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#

class antimatter.client.models.DomainIdentityProviderDetails(*args, **kwargs)#

Bases: pydantic.BaseModel

DomainIdentityProviderDetails

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: antimatter.client.models.google_o_auth_domain_identity_provider_details.GoogleOAuthDomainIdentityProviderDetails | None#

oneof_schema_2_validator: antimatter.client.models.api_key_domain_identity_provider_details.APIKeyDomainIdentityProviderDetails | None#

actual_instance: antimatter.client.models.api_key_domain_identity_provider_details.APIKeyDomainIdentityProviderDetails | antimatter.client.models.google_o_auth_domain_identity_provider_details.GoogleOAuthDomainIdentityProviderDetails | None#

one_of_schemas: List[str]#

model_config#

discriminator_value_class_map: Dict[str, str]#

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

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#

class antimatter.client.models.DomainIdentityProviderInfo(/, **data: Any)#

Bases: pydantic.BaseModel

Information about an identity provider. This may be an imported provider or a provider in this domain

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.

name: typing_extensions.Annotated[str, Field(strict=True)]#

type: antimatter.client.models.domain_identity_provider_type.DomainIdentityProviderType#

imported: pydantic.StrictBool#

source_domain_id: Optional[typing_extensions.Annotated[str, Field(strict=True)]]#

source_domain_name: pydantic.StrictStr | None#

supported_principals: List[antimatter.client.models.domain_identity_provider_principal_type.DomainIdentityProviderPrincipalType]#

details: antimatter.client.models.domain_identity_provider_details.DomainIdentityProviderDetails | None#

model_config#

name_validate_regular_expression(value)#: Validates the regular expression

source_domain_id_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainIdentityProviderInfo from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainIdentityProviderInfo from a dict

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

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#

class antimatter.client.models.DomainIdentityProviderList(/, **data: Any)#

Bases: pydantic.BaseModel

A list of identity providers

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.

identity_providers: List[antimatter.client.models.domain_identity_provider_info.DomainIdentityProviderInfo] | None#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainIdentityProviderList from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainIdentityProviderList from a dict

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

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#

class antimatter.client.models.DomainIdentityProviderPrincipalList(/, **data: Any)#

Bases: pydantic.BaseModel

A list of principals in an identity provider

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.

principals: List[antimatter.client.models.principal_summary.PrincipalSummary]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainIdentityProviderPrincipalList from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainIdentityProviderPrincipalList from a dict

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

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#

class antimatter.client.models.DomainIdentityProviderPrincipalParams(/, **data: Any)#

Bases: pydantic.BaseModel

Details to create a domain identity principal

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.

capabilities: List[antimatter.client.models.capability.Capability]#

details: antimatter.client.models.domain_identity_principal_details.DomainIdentityPrincipalDetails#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainIdentityProviderPrincipalParams from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainIdentityProviderPrincipalParams from a dict

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

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#

class antimatter.client.models.DomainIdentityProviderPrincipalType#

Bases: str, enum.Enum

Principal type supported by an identity provider

APIKEY = 'APIKey'#

EMAIL = 'Email'#

HOSTEDDOMAIN = 'HostedDomain'#

classmethod from_json(json_str: str) → Self#: Create an instance of DomainIdentityProviderPrincipalType from a JSON string

capitalize()#

Return a capitalized version of the string.

More specifically, make the first character have upper case and the rest lower case.

casefold()#: Return a version of the string suitable for caseless comparisons.

center()#

Return a centered string of length width.

Padding is done using the specified fill character (default is a space).

count()#

S.count(sub[, start[, end]]) -> int

Return the number of non-overlapping occurrences of substring sub in string S[start:end]. Optional arguments start and end are interpreted as in slice notation.

encode()#

Encode the string using the codec registered for encoding.

encoding: The encoding in which to encode the string.
errors: The error handling scheme to use for encoding errors. The default is ‘strict’ meaning that encoding errors raise a UnicodeEncodeError. Other possible values are ‘ignore’, ‘replace’ and ‘xmlcharrefreplace’ as well as any other name registered with codecs.register_error that can handle UnicodeEncodeErrors.

endswith()#

S.endswith(suffix[, start[, end]]) -> bool

Return True if S ends with the specified suffix, False otherwise. With optional start, test S beginning at that position. With optional end, stop comparing S at that position. suffix can also be a tuple of strings to try.

expandtabs()#

Return a copy where all tab characters are expanded using spaces.

If tabsize is not given, a tab size of 8 characters is assumed.

find()#

S.find(sub[, start[, end]]) -> int

Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Return -1 on failure.

format()#

S.format(*args, **kwargs) -> str

Return a formatted version of S, using substitutions from args and kwargs. The substitutions are identified by braces (‘{’ and ‘}’).

format_map()#

S.format_map(mapping) -> str

Return a formatted version of S, using substitutions from mapping. The substitutions are identified by braces (‘{’ and ‘}’).

index()#

S.index(sub[, start[, end]]) -> int

Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Raises ValueError when the substring is not found.

isalnum()#

Return True if the string is an alpha-numeric string, False otherwise.

A string is alpha-numeric if all characters in the string are alpha-numeric and there is at least one character in the string.

isalpha()#

Return True if the string is an alphabetic string, False otherwise.

A string is alphabetic if all characters in the string are alphabetic and there is at least one character in the string.

isascii()#

Return True if all characters in the string are ASCII, False otherwise.

ASCII characters have code points in the range U+0000-U+007F. Empty string is ASCII too.

isdecimal()#

Return True if the string is a decimal string, False otherwise.

A string is a decimal string if all characters in the string are decimal and there is at least one character in the string.

isdigit()#

Return True if the string is a digit string, False otherwise.

A string is a digit string if all characters in the string are digits and there is at least one character in the string.

isidentifier()#

Return True if the string is a valid Python identifier, False otherwise.

Call keyword.iskeyword(s) to test whether string s is a reserved identifier, such as “def” or “class”.

islower()#

Return True if the string is a lowercase string, False otherwise.

A string is lowercase if all cased characters in the string are lowercase and there is at least one cased character in the string.

isnumeric()#

Return True if the string is a numeric string, False otherwise.

A string is numeric if all characters in the string are numeric and there is at least one character in the string.

isprintable()#

Return True if the string is printable, False otherwise.

A string is printable if all of its characters are considered printable in repr() or if it is empty.

isspace()#

Return True if the string is a whitespace string, False otherwise.

A string is whitespace if all characters in the string are whitespace and there is at least one character in the string.

istitle()#

Return True if the string is a title-cased string, False otherwise.

In a title-cased string, upper- and title-case characters may only follow uncased characters and lowercase characters only cased ones.

isupper()#

Return True if the string is an uppercase string, False otherwise.

A string is uppercase if all cased characters in the string are uppercase and there is at least one cased character in the string.

join()#

Concatenate any number of strings.

The string whose method is called is inserted in between each given string. The result is returned as a new string.

Example: ‘.’.join([‘ab’, ‘pq’, ‘rs’]) -> ‘ab.pq.rs’

ljust()#

Return a left-justified string of length width.

Padding is done using the specified fill character (default is a space).

lower()#: Return a copy of the string converted to lowercase.

lstrip()#

Return a copy of the string with leading whitespace removed.

If chars is given and not None, remove characters in chars instead.

partition()#

Partition the string into three parts using the given separator.

This will search for the separator in the string. If the separator is found, returns a 3-tuple containing the part before the separator, the separator itself, and the part after it.

If the separator is not found, returns a 3-tuple containing the original string and two empty strings.

removeprefix()#

Return a str with the given prefix string removed if present.

If the string starts with the prefix string, return string[len(prefix):]. Otherwise, return a copy of the original string.

removesuffix()#

Return a str with the given suffix string removed if present.

If the string ends with the suffix string and that suffix is not empty, return string[:-len(suffix)]. Otherwise, return a copy of the original string.

replace()#

Return a copy with all occurrences of substring old replaced by new.

If the optional argument count is given, only the first count occurrences are replaced.

rfind()#

S.rfind(sub[, start[, end]]) -> int

Return the highest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Return -1 on failure.

rindex()#

S.rindex(sub[, start[, end]]) -> int

Return the highest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Raises ValueError when the substring is not found.

rjust()#

Return a right-justified string of length width.

Padding is done using the specified fill character (default is a space).

rpartition()#

Partition the string into three parts using the given separator.

This will search for the separator in the string, starting at the end. If the separator is found, returns a 3-tuple containing the part before the separator, the separator itself, and the part after it.

If the separator is not found, returns a 3-tuple containing two empty strings and the original string.

rsplit()#

Return a list of the substrings in the string, using sep as the separator string.

Splitting starts at the end of the string and works to the front.

rstrip()#

Return a copy of the string with trailing whitespace removed.

If chars is given and not None, remove characters in chars instead.

split()#

Return a list of the substrings in the string, using sep as the separator string.

Note, str.split() is mainly useful for data that has been intentionally delimited. With natural text that includes punctuation, consider using the regular expression module.

splitlines()#

Return a list of the lines in the string, breaking at line boundaries.

Line breaks are not included in the resulting list unless keepends is given and true.

startswith()#

S.startswith(prefix[, start[, end]]) -> bool

Return True if S starts with the specified prefix, False otherwise. With optional start, test S beginning at that position. With optional end, stop comparing S at that position. prefix can also be a tuple of strings to try.

strip()#

Return a copy of the string with leading and trailing whitespace removed.

If chars is given and not None, remove characters in chars instead.

swapcase()#: Convert uppercase characters to lowercase and lowercase characters to uppercase.

title()#

Return a version of the string where each word is titlecased.

More specifically, words start with uppercased characters and all remaining cased characters have lower case.

translate()#

Replace each character in the string using the given translation table.

The table must implement lookup/indexing via __getitem__, for instance a dictionary or list. If this operation raises LookupError, the character is left untouched. Characters mapped to None are deleted.

upper()#: Return a copy of the string converted to uppercase.

zfill()#

Pad a numeric string with zeros on the left, to fill a field of the given width.

The string is never truncated.

name()#: The name of the Enum member.

value()#: The value of the Enum member.

class antimatter.client.models.DomainIdentityProviderType#

Bases: str, enum.Enum

Type of the identity provider.

GOOGLEOAUTH = 'GoogleOAuth'#

GCPSERVICEACCOUNT = 'GCPServiceAccount'#

APIKEY = 'APIKey'#

classmethod from_json(json_str: str) → Self#: Create an instance of DomainIdentityProviderType from a JSON string

capitalize()#

Return a capitalized version of the string.

More specifically, make the first character have upper case and the rest lower case.

casefold()#: Return a version of the string suitable for caseless comparisons.

center()#

Return a centered string of length width.

Padding is done using the specified fill character (default is a space).

count()#

S.count(sub[, start[, end]]) -> int

Return the number of non-overlapping occurrences of substring sub in string S[start:end]. Optional arguments start and end are interpreted as in slice notation.

encode()#

Encode the string using the codec registered for encoding.

encoding: The encoding in which to encode the string.
errors: The error handling scheme to use for encoding errors. The default is ‘strict’ meaning that encoding errors raise a UnicodeEncodeError. Other possible values are ‘ignore’, ‘replace’ and ‘xmlcharrefreplace’ as well as any other name registered with codecs.register_error that can handle UnicodeEncodeErrors.

endswith()#

S.endswith(suffix[, start[, end]]) -> bool

Return True if S ends with the specified suffix, False otherwise. With optional start, test S beginning at that position. With optional end, stop comparing S at that position. suffix can also be a tuple of strings to try.

expandtabs()#

Return a copy where all tab characters are expanded using spaces.

If tabsize is not given, a tab size of 8 characters is assumed.

find()#

S.find(sub[, start[, end]]) -> int

Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Return -1 on failure.

format()#

S.format(*args, **kwargs) -> str

Return a formatted version of S, using substitutions from args and kwargs. The substitutions are identified by braces (‘{’ and ‘}’).

format_map()#

S.format_map(mapping) -> str

Return a formatted version of S, using substitutions from mapping. The substitutions are identified by braces (‘{’ and ‘}’).

index()#

S.index(sub[, start[, end]]) -> int

Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Raises ValueError when the substring is not found.

isalnum()#

Return True if the string is an alpha-numeric string, False otherwise.

A string is alpha-numeric if all characters in the string are alpha-numeric and there is at least one character in the string.

isalpha()#

Return True if the string is an alphabetic string, False otherwise.

A string is alphabetic if all characters in the string are alphabetic and there is at least one character in the string.

isascii()#

Return True if all characters in the string are ASCII, False otherwise.

ASCII characters have code points in the range U+0000-U+007F. Empty string is ASCII too.

isdecimal()#

Return True if the string is a decimal string, False otherwise.

A string is a decimal string if all characters in the string are decimal and there is at least one character in the string.

isdigit()#

Return True if the string is a digit string, False otherwise.

A string is a digit string if all characters in the string are digits and there is at least one character in the string.

isidentifier()#

Return True if the string is a valid Python identifier, False otherwise.

Call keyword.iskeyword(s) to test whether string s is a reserved identifier, such as “def” or “class”.

islower()#

Return True if the string is a lowercase string, False otherwise.

A string is lowercase if all cased characters in the string are lowercase and there is at least one cased character in the string.

isnumeric()#

Return True if the string is a numeric string, False otherwise.

A string is numeric if all characters in the string are numeric and there is at least one character in the string.

isprintable()#

Return True if the string is printable, False otherwise.

A string is printable if all of its characters are considered printable in repr() or if it is empty.

isspace()#

Return True if the string is a whitespace string, False otherwise.

A string is whitespace if all characters in the string are whitespace and there is at least one character in the string.

istitle()#

Return True if the string is a title-cased string, False otherwise.

In a title-cased string, upper- and title-case characters may only follow uncased characters and lowercase characters only cased ones.

isupper()#

Return True if the string is an uppercase string, False otherwise.

A string is uppercase if all cased characters in the string are uppercase and there is at least one cased character in the string.

join()#

Concatenate any number of strings.

The string whose method is called is inserted in between each given string. The result is returned as a new string.

Example: ‘.’.join([‘ab’, ‘pq’, ‘rs’]) -> ‘ab.pq.rs’

ljust()#

Return a left-justified string of length width.

Padding is done using the specified fill character (default is a space).

lower()#: Return a copy of the string converted to lowercase.

lstrip()#

Return a copy of the string with leading whitespace removed.

If chars is given and not None, remove characters in chars instead.

partition()#

Partition the string into three parts using the given separator.

This will search for the separator in the string. If the separator is found, returns a 3-tuple containing the part before the separator, the separator itself, and the part after it.

If the separator is not found, returns a 3-tuple containing the original string and two empty strings.

removeprefix()#

Return a str with the given prefix string removed if present.

If the string starts with the prefix string, return string[len(prefix):]. Otherwise, return a copy of the original string.

removesuffix()#

Return a str with the given suffix string removed if present.

If the string ends with the suffix string and that suffix is not empty, return string[:-len(suffix)]. Otherwise, return a copy of the original string.

replace()#

Return a copy with all occurrences of substring old replaced by new.

If the optional argument count is given, only the first count occurrences are replaced.

rfind()#

S.rfind(sub[, start[, end]]) -> int

Return the highest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Return -1 on failure.

rindex()#

S.rindex(sub[, start[, end]]) -> int

Return the highest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Raises ValueError when the substring is not found.

rjust()#

Return a right-justified string of length width.

Padding is done using the specified fill character (default is a space).

rpartition()#

Partition the string into three parts using the given separator.

This will search for the separator in the string, starting at the end. If the separator is found, returns a 3-tuple containing the part before the separator, the separator itself, and the part after it.

If the separator is not found, returns a 3-tuple containing two empty strings and the original string.

rsplit()#

Return a list of the substrings in the string, using sep as the separator string.

Splitting starts at the end of the string and works to the front.

rstrip()#

Return a copy of the string with trailing whitespace removed.

If chars is given and not None, remove characters in chars instead.

split()#

Return a list of the substrings in the string, using sep as the separator string.

Note, str.split() is mainly useful for data that has been intentionally delimited. With natural text that includes punctuation, consider using the regular expression module.

splitlines()#

Return a list of the lines in the string, breaking at line boundaries.

Line breaks are not included in the resulting list unless keepends is given and true.

startswith()#

S.startswith(prefix[, start[, end]]) -> bool

Return True if S starts with the specified prefix, False otherwise. With optional start, test S beginning at that position. With optional end, stop comparing S at that position. prefix can also be a tuple of strings to try.

strip()#

Return a copy of the string with leading and trailing whitespace removed.

If chars is given and not None, remove characters in chars instead.

swapcase()#: Convert uppercase characters to lowercase and lowercase characters to uppercase.

title()#

Return a version of the string where each word is titlecased.

More specifically, words start with uppercased characters and all remaining cased characters have lower case.

translate()#

Replace each character in the string using the given translation table.

The table must implement lookup/indexing via __getitem__, for instance a dictionary or list. If this operation raises LookupError, the character is left untouched. Characters mapped to None are deleted.

upper()#: Return a copy of the string converted to uppercase.

zfill()#

Pad a numeric string with zeros on the left, to fill a field of the given width.

The string is never truncated.

name()#: The name of the Enum member.

value()#: The value of the Enum member.

class antimatter.client.models.DomainInsertIdentityProviderPrincipal200Response(/, **data: Any)#

Bases: pydantic.BaseModel

DomainInsertIdentityProviderPrincipal200Response

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.

principal_id: typing_extensions.Annotated[str, Field(strict=True)]#

api_key: pydantic.StrictStr | None#

model_config#

principal_id_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainInsertIdentityProviderPrincipal200Response from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainInsertIdentityProviderPrincipal200Response from a dict

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

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#

class antimatter.client.models.DomainInsertWriteContextRegexRule200Response(/, **data: Any)#

Bases: pydantic.BaseModel

DomainInsertWriteContextRegexRule200Response

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.

rule_id: typing_extensions.Annotated[str, Field(strict=True)]#

model_config#

rule_id_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainInsertWriteContextRegexRule200Response from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainInsertWriteContextRegexRule200Response from a dict

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

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#

class antimatter.client.models.DomainPeerConfig(/, **data: Any)#

Bases: pydantic.BaseModel

Configuration of a domain peer. If the import alias is absent, the domain ID, without the initial “dm-” prefix, will be used

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.

export_identity_providers: Optional[List[typing_extensions.Annotated[str, Field(strict=True)]]]#

export_all_identity_providers: pydantic.StrictBool | None#

export_facts: Optional[List[typing_extensions.Annotated[str, Field(strict=True)]]]#

export_all_facts: pydantic.StrictBool | None#

export_read_contexts: Optional[List[typing_extensions.Annotated[str, Field(strict=True)]]]#

export_all_read_contexts: pydantic.StrictBool | None#

export_write_contexts: Optional[List[typing_extensions.Annotated[str, Field(strict=True)]]]#

export_all_write_contexts: pydantic.StrictBool | None#

export_capabilities: Optional[List[typing_extensions.Annotated[str, Field(strict=True)]]]#

export_all_capabilities: pydantic.StrictBool | None#

export_domain_policy: pydantic.StrictBool | None#

export_capsule_access_log: pydantic.StrictBool | None#

export_control_log: pydantic.StrictBool | None#

export_capsule_manifest: pydantic.StrictBool | None#

export_billing: pydantic.StrictBool | None#

export_admin_contact: pydantic.StrictBool | None#

nicknames: Optional[List[typing_extensions.Annotated[str, Field(strict=True, max_length=128)]]]#

import_alias: Optional[typing_extensions.Annotated[str, Field(strict=True)]]#

forward_billing: pydantic.StrictBool | None#

forward_admin_communications: pydantic.StrictBool | None#

import_identity_providers: Optional[List[typing_extensions.Annotated[str, Field(strict=True)]]]#

import_all_identity_providers: pydantic.StrictBool | None#

import_facts: Optional[List[typing_extensions.Annotated[str, Field(strict=True)]]]#

import_all_facts: pydantic.StrictBool | None#

import_read_contexts: Optional[List[typing_extensions.Annotated[str, Field(strict=True)]]]#

import_all_read_contexts: pydantic.StrictBool | None#

import_write_contexts: Optional[List[typing_extensions.Annotated[str, Field(strict=True)]]]#

import_all_write_contexts: pydantic.StrictBool | None#

import_capabilities: Optional[List[typing_extensions.Annotated[str, Field(strict=True)]]]#

import_all_capabilities: pydantic.StrictBool | None#

import_domain_policy: pydantic.StrictBool | None#

import_precedence: pydantic.StrictInt | None#

import_capsule_access_log: pydantic.StrictBool | None#

import_control_log: pydantic.StrictBool | None#

import_capsule_manifest: pydantic.StrictBool | None#

display_name: typing_extensions.Annotated[str, Field(min_length=1, strict=True, max_length=40)]#

model_config#

import_alias_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainPeerConfig from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainPeerConfig from a dict

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

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#

class antimatter.client.models.DomainPeerList(/, **data: Any)#

Bases: pydantic.BaseModel

Information about the domains that this domain is peered with

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.

peers: List[antimatter.client.models.domain_peer_list_peers_inner.DomainPeerListPeersInner]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainPeerList from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainPeerList from a dict

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

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#

class antimatter.client.models.DomainPeerListPeersInner(/, **data: Any)#

Bases: pydantic.BaseModel

DomainPeerListPeersInner

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.

id: typing_extensions.Annotated[str, Field(strict=True)]#

alias: typing_extensions.Annotated[str, Field(strict=True)]#

model_config#

id_validate_regular_expression(value)#: Validates the regular expression

alias_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainPeerListPeersInner from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainPeerListPeersInner from a dict

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

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#

class antimatter.client.models.DomainPolicy(/, **data: Any)#

Bases: pydantic.BaseModel

A domain’s policy. These rules govern who can view, edit or use which parts of a domain’s configuration. Rules are executed in order of ascending priority number, and the execution stops with the first matching rule. If no rules match, the default action is ‘deny’. If domain edit policy rules are imported from other domains in the peering configuration, the rules in those domains are independently evaluated to yield an allow/deny result and the final result from every domain, including this one, will be ANDed together. Thus, a deny in any domain yields an overall deny, and allow is only returned if all domains return allow.

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.

rules: List[antimatter.client.models.domain_policy_rule.DomainPolicyRule]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainPolicy from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainPolicy from a dict

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

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#

class antimatter.client.models.DomainPolicyRule(/, **data: Any)#

Bases: pydantic.BaseModel

A rule governing the domain’s policy. All domain identity capabilities must match (AND) for the action to take effect. If the domainIdentity or facts sections are omitted, they match all domain identities and any fact configurations respectively. When updating or creating a rule, the id field may be omitted.

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.

id: Optional[typing_extensions.Annotated[str, Field(strict=True)]]#

domain_identity: antimatter.client.models.capability_rule.CapabilityRule | None#

facts: List[antimatter.client.models.fact_policy_rules_inner.FactPolicyRulesInner] | None#

path: pydantic.StrictStr#

operation: pydantic.StrictStr#

result: pydantic.StrictStr#

priority: typing_extensions.Annotated[int, Field(strict=True, ge=0)]#

disabled: pydantic.StrictBool#

model_config#

id_validate_regular_expression(value)#: Validates the regular expression

operation_validate_enum(value)#: Validates the enum

result_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainPolicyRule from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainPolicyRule from a dict

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

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#

class antimatter.client.models.DomainPrivateInfo(/, **data: Any)#

Bases: pydantic.BaseModel

Private information about a domain

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.

default_display_name: typing_extensions.Annotated[str, Field(strict=True, max_length=40)]#

id: typing_extensions.Annotated[str, Field(strict=True)]#

identity_providers: List[antimatter.client.models.domain_identity_provider_info.DomainIdentityProviderInfo]#

model_config#

id_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainPrivateInfo from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainPrivateInfo from a dict

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

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#

class antimatter.client.models.DomainPublicInfo(/, **data: Any)#

Bases: pydantic.BaseModel

Public information about a domain

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.

default_display_name: typing_extensions.Annotated[str, Field(strict=True, max_length=40)]#

id: typing_extensions.Annotated[str, Field(strict=True)]#

identity_providers: List[antimatter.client.models.domain_identity_provider_info.DomainIdentityProviderInfo]#

model_config#

id_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainPublicInfo from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainPublicInfo from a dict

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

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#

class antimatter.client.models.DomainResourceSummary(/, **data: Any)#

Bases: pydantic.BaseModel

A list of the resources and permissions available

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.

var_schema: List[antimatter.client.models.domain_resource_summary_schema_inner.DomainResourceSummarySchemaInner]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainResourceSummary from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainResourceSummary from a dict

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

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#

class antimatter.client.models.DomainResourceSummarySchemaInner(/, **data: Any)#

Bases: pydantic.BaseModel

DomainResourceSummarySchemaInner

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.

resource: pydantic.StrictStr#

operations: List[pydantic.StrictStr]#

placeholder_values: Dict[str, List[pydantic.StrictStr]]#

description: pydantic.StrictStr#

model_config#

operations_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainResourceSummarySchemaInner from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainResourceSummarySchemaInner from a dict

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

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#

class antimatter.client.models.DomainSettings(/, **data: Any)#

Bases: pydantic.BaseModel

Additional configuration options for a domain

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.

disaster_recovery: antimatter.client.models.domain_settings_disaster_recovery.DomainSettingsDisasterRecovery | None#

admin_contacts: List[pydantic.StrictStr]#

active_admin_contacts: List[pydantic.StrictStr] | None#

pending_admin_contacts: List[pydantic.StrictStr] | None#

default_display_name: typing_extensions.Annotated[str, Field(strict=True, max_length=40)]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainSettings from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainSettings from a dict

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

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#

class antimatter.client.models.DomainSettingsDisasterRecovery(/, **data: Any)#

Bases: pydantic.BaseModel

DomainSettingsDisasterRecovery

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.

enable: pydantic.StrictBool | None#

public_key: pydantic.StrictStr | None#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainSettingsDisasterRecovery from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainSettingsDisasterRecovery from a dict

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

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#

class antimatter.client.models.DomainSettingsPatch(/, **data: Any)#

Bases: pydantic.BaseModel

A JSON patch to apply to the domain settings

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.

patch: List[antimatter.client.models.patch_request_inner.PatchRequestInner]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainSettingsPatch from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainSettingsPatch from a dict

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

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#

class antimatter.client.models.DomainStatus(/, **data: Any)#

Bases: pydantic.BaseModel

Information about the status of the domain

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.

notifications: List[antimatter.client.models.domain_status_notifications_inner.DomainStatusNotificationsInner] | None#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainStatus from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainStatus from a dict

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

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#

class antimatter.client.models.DomainStatusNotificationsInner(/, **data: Any)#

Bases: pydantic.BaseModel

DomainStatusNotificationsInner

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.

summary: pydantic.StrictStr#

description: pydantic.StrictStr#

type: pydantic.StrictStr#

model_config#

type_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainStatusNotificationsInner from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainStatusNotificationsInner from a dict

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

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#

class antimatter.client.models.DomainTagInfoResults(/, **data: Any)#

Bases: pydantic.BaseModel

Ordered list of the top 100 tags.

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.

tags: List[antimatter.client.models.tag_meta.TagMeta]#

has_more: pydantic.StrictBool#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of DomainTagInfoResults from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of DomainTagInfoResults from a dict

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

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#

class antimatter.client.models.Error(/, **data: Any)#

Bases: pydantic.BaseModel

An internal error

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.

trace_id: pydantic.StrictStr#

message: pydantic.StrictStr#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of Error from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of Error from a dict

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

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#

class antimatter.client.models.Fact(/, **data: Any)#

Bases: pydantic.BaseModel

A fact is a piece of auxiliary information that can be used as part of an authorization policy. They are usually expressed as a statement such as has_role(principal, role_name)

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.

id: typing_extensions.Annotated[str, Field(strict=True)]#

name: typing_extensions.Annotated[str, Field(strict=True)]#

arguments: typing_extensions.Annotated[List[typing_extensions.Annotated[str, Field(strict=True, max_length=256)]], Field(min_length=1, max_length=16)]#

model_config#

id_validate_regular_expression(value)#: Validates the regular expression

name_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of Fact from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of Fact from a dict

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

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#

class antimatter.client.models.FactList(/, **data: Any)#

Bases: pydantic.BaseModel

A list of facts

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.

facts: List[antimatter.client.models.fact.Fact]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of FactList from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of FactList from a dict

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

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#

class antimatter.client.models.FactPolicyRulesInner(/, **data: Any)#

Bases: pydantic.BaseModel

FactPolicyRulesInner

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.

operator: pydantic.StrictStr#

name: typing_extensions.Annotated[str, Field(strict=True)]#

arguments: typing_extensions.Annotated[List[antimatter.client.models.fact_policy_rules_inner_arguments_inner.FactPolicyRulesInnerArgumentsInner], Field(max_length=16)]#

model_config#

operator_validate_enum(value)#: Validates the enum

name_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of FactPolicyRulesInner from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of FactPolicyRulesInner from a dict

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

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#

class antimatter.client.models.FactPolicyRulesInnerArgumentsInner(/, **data: Any)#

Bases: pydantic.BaseModel

FactPolicyRulesInnerArgumentsInner

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.

any: pydantic.StrictBool | None#

source: pydantic.StrictStr | None#

capability: Optional[typing_extensions.Annotated[str, Field(strict=True)]]#

value: pydantic.StrictStr | None#

model_config#

source_validate_enum(value)#: Validates the enum

capability_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of FactPolicyRulesInnerArgumentsInner from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of FactPolicyRulesInnerArgumentsInner from a dict

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

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#

class antimatter.client.models.FactTypeDefinition(/, **data: Any)#

Bases: pydantic.BaseModel

A type definition (schema) for a fact

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.

name: typing_extensions.Annotated[str, Field(strict=True)]#

description: typing_extensions.Annotated[str, Field(strict=True, max_length=4096)]#

arguments: typing_extensions.Annotated[List[antimatter.client.models.new_fact_type_definition_arguments_inner.NewFactTypeDefinitionArgumentsInner], Field(min_length=1, max_length=16)]#

imported: pydantic.StrictBool#

source_domain_id: Optional[typing_extensions.Annotated[str, Field(strict=True)]]#

source_domain_name: pydantic.StrictStr | None#

model_config#

name_validate_regular_expression(value)#: Validates the regular expression

source_domain_id_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of FactTypeDefinition from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of FactTypeDefinition from a dict

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

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#

class antimatter.client.models.GCPServiceAccountKeyInfo(/, **data: Any)#

Bases: pydantic.BaseModel

The GCP service account information and details required to use the provided GCP hosted encryption key for cryptographic operations.

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.

service_account_credentials: pydantic.StrictBytes | pydantic.StrictStr#

project_id: pydantic.StrictStr#

location: pydantic.StrictStr#

keyring_id: pydantic.StrictStr#

key_id: pydantic.StrictStr#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of GCPServiceAccountKeyInfo from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of GCPServiceAccountKeyInfo from a dict

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

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#

class antimatter.client.models.GoogleOAuthDomainIdentityProviderDetails(/, **data: Any)#

Bases: pydantic.BaseModel

Detailed information about a Google OAuth identity provider. If the clientID is omitted, an Antimatter Client ID will be used.

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.

type: pydantic.StrictStr#

client_id: pydantic.StrictStr | None#

model_config#

type_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of GoogleOAuthDomainIdentityProviderDetails from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of GoogleOAuthDomainIdentityProviderDetails from a dict

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

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#

class antimatter.client.models.HookInvocation(/, **data: Any)#

Bases: pydantic.BaseModel

The name and version of a hook that has been invoked on a capsule.

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.

name: pydantic.StrictStr#

version: pydantic.StrictStr#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of HookInvocation from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of HookInvocation from a dict

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

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#

class antimatter.client.models.InvalidRequestError(/, **data: Any)#

Bases: pydantic.BaseModel

Returned when one of the identifiers or arguments in the request is invalid

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.

field: pydantic.StrictStr#

message: pydantic.StrictStr#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of InvalidRequestError from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of InvalidRequestError from a dict

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

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#

class antimatter.client.models.JSONPatchRequestAdd(/, **data: Any)#

Bases: pydantic.BaseModel

JSONPatchRequestAdd

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.

path: pydantic.StrictStr#

value: antimatter.client.models.json_patch_request_add_value.JSONPatchRequestAddValue#

op: pydantic.StrictStr#

model_config#

op_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of JSONPatchRequestAdd from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of JSONPatchRequestAdd from a dict

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

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#

class antimatter.client.models.JSONPatchRequestAddValue(*args, **kwargs)#

Bases: pydantic.BaseModel

The value to add.

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

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#

class antimatter.client.models.JSONPatchRequestCopy(/, **data: Any)#

Bases: pydantic.BaseModel

JSONPatchRequestCopy

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.

path: pydantic.StrictStr#

op: pydantic.StrictStr#

model_config#

op_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of JSONPatchRequestCopy from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of JSONPatchRequestCopy from a dict

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

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#

class antimatter.client.models.JSONPatchRequestMove(/, **data: Any)#

Bases: pydantic.BaseModel

JSONPatchRequestMove

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.

path: pydantic.StrictStr#

op: pydantic.StrictStr#

model_config#

op_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of JSONPatchRequestMove from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of JSONPatchRequestMove from a dict

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

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#

class antimatter.client.models.JSONPatchRequestRemove(/, **data: Any)#

Bases: pydantic.BaseModel

JSONPatchRequestRemove

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.

path: pydantic.StrictStr#

op: pydantic.StrictStr#

model_config#

op_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of JSONPatchRequestRemove from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of JSONPatchRequestRemove from a dict

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

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#

class antimatter.client.models.JSONPatchRequestReplace(/, **data: Any)#

Bases: pydantic.BaseModel

JSONPatchRequestReplace

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.

path: pydantic.StrictStr#

value: antimatter.client.models.json_patch_request_replace_value.JSONPatchRequestReplaceValue#

op: pydantic.StrictStr#

model_config#

op_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of JSONPatchRequestReplace from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of JSONPatchRequestReplace from a dict

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

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#

class antimatter.client.models.JSONPatchRequestReplaceValue(*args, **kwargs)#

Bases: pydantic.BaseModel

The value to replace.

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

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#

class antimatter.client.models.JSONPatchRequestTst(/, **data: Any)#

Bases: pydantic.BaseModel

JSONPatchRequestTst

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.

path: pydantic.StrictStr#

value: antimatter.client.models.json_patch_request_tst_value.JSONPatchRequestTstValue#

op: pydantic.StrictStr#

model_config#

op_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of JSONPatchRequestTst from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of JSONPatchRequestTst from a dict

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

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#

class antimatter.client.models.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

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#

class antimatter.client.models.KeyInfos(/, **data: Any)#

Bases: pydantic.BaseModel

Holds the required service account information for varying providers.

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.

description: pydantic.StrictStr | None#

key_information: antimatter.client.models.key_infos_key_information.KeyInfosKeyInformation#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of KeyInfos from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of KeyInfos from a dict

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

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#

class antimatter.client.models.KeyInfosKeyInformation(*args, **kwargs)#

Bases: pydantic.BaseModel

KeyInfosKeyInformation

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: antimatter.client.models.gcp_service_account_key_info.GCPServiceAccountKeyInfo | None#

oneof_schema_2_validator: antimatter.client.models.aws_service_account_key_info.AWSServiceAccountKeyInfo | None#

oneof_schema_3_validator: antimatter.client.models.antimatter_delegated_aws_key_info.AntimatterDelegatedAWSKeyInfo | None#

actual_instance: antimatter.client.models.aws_service_account_key_info.AWSServiceAccountKeyInfo | antimatter.client.models.antimatter_delegated_aws_key_info.AntimatterDelegatedAWSKeyInfo | antimatter.client.models.gcp_service_account_key_info.GCPServiceAccountKeyInfo | None#

one_of_schemas: List[str]#

model_config#

discriminator_value_class_map: Dict[str, str]#

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

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#

class antimatter.client.models.NewAccessLogEntry(/, **data: Any)#

Bases: pydantic.BaseModel

An individual capsule data-plane log entry, in the form required when inserting a new record

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.

operation: pydantic.StrictStr#

location: pydantic.StrictStr | None#

read_info: antimatter.client.models.new_access_log_entry_read_info.NewAccessLogEntryReadInfo#

model_config#

operation_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of NewAccessLogEntry from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of NewAccessLogEntry from a dict

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

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#

class antimatter.client.models.NewAccessLogEntryReadInfo(/, **data: Any)#

Bases: pydantic.BaseModel

information available if the operation is of type “read”. allowedTags are those that were allowed without transformation during the read. redactedTags are those that were redacted during the read. tokenizedTags are those that were tokenized during the read.

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.

parameters: Dict[str, pydantic.StrictStr]#

allowed_tags: antimatter.client.models.tag_summary.TagSummary#

redacted_tags: antimatter.client.models.tag_summary.TagSummary#

tokenized_tags: antimatter.client.models.tag_summary.TagSummary#

returned_records: pydantic.StrictInt#

filtered_records: pydantic.StrictInt#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of NewAccessLogEntryReadInfo from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of NewAccessLogEntryReadInfo from a dict

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

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#

class antimatter.client.models.NewCapabilityDefinition(/, **data: Any)#

Bases: pydantic.BaseModel

A capability is attached to authenticated domain identities by an identity provider, and confers additional permissions upon the identity. This is done by writing domain policy rules that reference the capability.

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.

unary: pydantic.StrictBool#

summary: typing_extensions.Annotated[str, Field(strict=True, max_length=140)]#

description: typing_extensions.Annotated[str, Field(strict=True, max_length=4096)]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of NewCapabilityDefinition from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of NewCapabilityDefinition from a dict

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

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#

class antimatter.client.models.NewDomain(/, **data: Any)#

Bases: pydantic.BaseModel

Parameters when creating a domain

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.

admin_email: typing_extensions.Annotated[str, Field(min_length=6, strict=True)]#

display_name: Optional[typing_extensions.Annotated[str, Field(strict=True, max_length=40)]]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of NewDomain from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of NewDomain from a dict

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

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#

class antimatter.client.models.NewDomainResponse(/, **data: Any)#

Bases: pydantic.BaseModel

Information returned from a successful domain create request

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.

id: typing_extensions.Annotated[str, Field(strict=True)]#

api_key: pydantic.StrictStr#

model_config#

id_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of NewDomainResponse from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of NewDomainResponse from a dict

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

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#

class antimatter.client.models.NewFact(/, **data: Any)#

Bases: pydantic.BaseModel

A fact is a piece of auxiliary information that can be used as part of an authorization policy. They are usually expressed as a statement such as has_role(principal, role_name)

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.

arguments: typing_extensions.Annotated[List[typing_extensions.Annotated[str, Field(strict=True, max_length=256)]], Field(min_length=1, max_length=16)]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of NewFact from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of NewFact from a dict

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

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#

class antimatter.client.models.NewFactTypeDefinition(/, **data: Any)#

Bases: pydantic.BaseModel

A type definition (schema) for a fact being created

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.

description: typing_extensions.Annotated[str, Field(strict=True, max_length=4096)]#

arguments: typing_extensions.Annotated[List[antimatter.client.models.new_fact_type_definition_arguments_inner.NewFactTypeDefinitionArgumentsInner], Field(min_length=1, max_length=16)]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of NewFactTypeDefinition from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of NewFactTypeDefinition from a dict

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

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#

class antimatter.client.models.NewFactTypeDefinitionArgumentsInner(/, **data: Any)#

Bases: pydantic.BaseModel

NewFactTypeDefinitionArgumentsInner

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.

name: typing_extensions.Annotated[str, Field(min_length=1, strict=True, max_length=32)]#

description: typing_extensions.Annotated[str, Field(strict=True, max_length=128)]#

model_config#

name_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of NewFactTypeDefinitionArgumentsInner from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of NewFactTypeDefinitionArgumentsInner from a dict

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

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#

class antimatter.client.models.NewReadContextConfigRule(/, **data: Any)#

Bases: pydantic.BaseModel

Information about what must be done to data when it is read from a capsule

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.

match_expressions: List[antimatter.client.models.read_context_rule_match_expressions_inner.ReadContextRuleMatchExpressionsInner] | None#

action: pydantic.StrictStr#

token_scope: pydantic.StrictStr | None#

token_format: pydantic.StrictStr | None#

facts: List[antimatter.client.models.read_context_rule_facts_inner.ReadContextRuleFactsInner] | None#

priority: typing_extensions.Annotated[int, Field(strict=True, ge=0)]#

model_config#

action_validate_enum(value)#: Validates the enum

token_scope_validate_enum(value)#: Validates the enum

token_format_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of NewReadContextConfigRule from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of NewReadContextConfigRule from a dict

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

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#

class antimatter.client.models.PatchRequestInner(*args, **kwargs)#

Bases: pydantic.BaseModel

PatchRequestInner

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: antimatter.client.models.json_patch_request_add.JSONPatchRequestAdd | None#

oneof_schema_2_validator: antimatter.client.models.json_patch_request_replace.JSONPatchRequestReplace | None#

oneof_schema_3_validator: antimatter.client.models.json_patch_request_tst.JSONPatchRequestTst | None#

oneof_schema_4_validator: antimatter.client.models.json_patch_request_remove.JSONPatchRequestRemove | None#

oneof_schema_5_validator: antimatter.client.models.json_patch_request_move.JSONPatchRequestMove | None#

oneof_schema_6_validator: antimatter.client.models.json_patch_request_copy.JSONPatchRequestCopy | None#

actual_instance: antimatter.client.models.json_patch_request_add.JSONPatchRequestAdd | antimatter.client.models.json_patch_request_copy.JSONPatchRequestCopy | antimatter.client.models.json_patch_request_move.JSONPatchRequestMove | antimatter.client.models.json_patch_request_remove.JSONPatchRequestRemove | antimatter.client.models.json_patch_request_replace.JSONPatchRequestReplace | antimatter.client.models.json_patch_request_tst.JSONPatchRequestTst | None#

one_of_schemas: List[str]#

model_config#

discriminator_value_class_map: Dict[str, str]#

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

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#

class antimatter.client.models.PrincipalInfo(/, **data: Any)#

Bases: pydantic.BaseModel

Detailed information about a principal

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.

principal_id: typing_extensions.Annotated[str, Field(strict=True)]#

capabilities: List[antimatter.client.models.capability.Capability]#

details: antimatter.client.models.domain_identity_principal_details.DomainIdentityPrincipalDetails#

model_config#

principal_id_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of PrincipalInfo from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of PrincipalInfo from a dict

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

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#

class antimatter.client.models.PrincipalSummary(/, **data: Any)#

Bases: pydantic.BaseModel

PrincipalSummary

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.

principal_id: typing_extensions.Annotated[str, Field(strict=True)]#

principal_type: antimatter.client.models.domain_identity_provider_principal_type.DomainIdentityProviderPrincipalType#

model_config#

principal_id_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of PrincipalSummary from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of PrincipalSummary from a dict

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

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#

class antimatter.client.models.ReadContextConfigRule(/, **data: Any)#

Bases: pydantic.BaseModel

Information about what must be done to data when it is read from a capsule

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.

id: typing_extensions.Annotated[str, Field(strict=True)]#

match_expressions: List[antimatter.client.models.read_context_rule_match_expressions_inner.ReadContextRuleMatchExpressionsInner] | None#

action: pydantic.StrictStr#

token_scope: pydantic.StrictStr | None#

token_format: pydantic.StrictStr | None#

facts: List[antimatter.client.models.read_context_rule_facts_inner.ReadContextRuleFactsInner] | None#

priority: typing_extensions.Annotated[int, Field(strict=True, ge=0)]#

imported: pydantic.StrictBool#

source_domain_id: Optional[typing_extensions.Annotated[str, Field(strict=True)]]#

source_domain_name: pydantic.StrictStr | None#

model_config#

id_validate_regular_expression(value)#: Validates the regular expression

action_validate_enum(value)#: Validates the enum

token_scope_validate_enum(value)#: Validates the enum

token_format_validate_enum(value)#: Validates the enum

source_domain_id_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of ReadContextConfigRule from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of ReadContextConfigRule from a dict

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

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#

class antimatter.client.models.ReadContextDetails(/, **data: Any)#

Bases: pydantic.BaseModel

Details about a read context

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.

name: typing_extensions.Annotated[str, Field(strict=True)]#

summary: typing_extensions.Annotated[str, Field(strict=True, max_length=140)]#

description: typing_extensions.Annotated[str, Field(strict=True, max_length=4096)]#

disable_read_logging: pydantic.StrictBool | None#

key_cache_ttl: Optional[typing_extensions.Annotated[int, Field(strict=True, ge=0)]]#

required_hooks: List[antimatter.client.models.read_context_required_hook.ReadContextRequiredHook] | None#

read_parameters: List[antimatter.client.models.read_context_parameter.ReadContextParameter]#

rules: List[antimatter.client.models.read_context_config_rule.ReadContextConfigRule]#

imported: pydantic.StrictBool#

source_domain_id: Optional[typing_extensions.Annotated[str, Field(strict=True)]]#

source_domain_name: pydantic.StrictStr | None#

model_config#

name_validate_regular_expression(value)#: Validates the regular expression

source_domain_id_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of ReadContextDetails from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of ReadContextDetails from a dict

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

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#

class antimatter.client.models.ReadContextList(/, **data: Any)#

Bases: pydantic.BaseModel

A list of read contexts

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.

read_contexts: List[antimatter.client.models.read_context_short_details.ReadContextShortDetails]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of ReadContextList from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of ReadContextList from a dict

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

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#

class antimatter.client.models.ReadContextParameter(/, **data: Any)#

Bases: pydantic.BaseModel

Declare parameters that can be passed in for use in read context configuration rules. It is expected that these are used for distinguishing who a read is being done on behalf of, and important attributes about that user (team, project, org etc).

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.

key: pydantic.StrictStr | None#

required: pydantic.StrictBool | None#

description: pydantic.StrictStr | None#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of ReadContextParameter from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of ReadContextParameter from a dict

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

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#

class antimatter.client.models.ReadContextRequiredHook(/, **data: Any)#

Bases: pydantic.BaseModel

ReadContextRequiredHook

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.

hook: Optional[typing_extensions.Annotated[str, Field(strict=True)]]#

constraint: Optional[typing_extensions.Annotated[str, Field(strict=True)]]#

write_context: Optional[typing_extensions.Annotated[str, Field(strict=True)]]#

model_config#

hook_validate_regular_expression(value)#: Validates the regular expression

constraint_validate_regular_expression(value)#: Validates the regular expression

write_context_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of ReadContextRequiredHook from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of ReadContextRequiredHook from a dict

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

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#

class antimatter.client.models.ReadContextRuleFactsInner(/, **data: Any)#

Bases: pydantic.BaseModel

ReadContextRuleFactsInner

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.

operator: pydantic.StrictStr | None#

name: pydantic.StrictStr | None#

arguments: List[antimatter.client.models.read_context_rule_facts_inner_arguments_inner.ReadContextRuleFactsInnerArgumentsInner] | None#

model_config#

operator_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of ReadContextRuleFactsInner from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of ReadContextRuleFactsInner from a dict

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

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#

class antimatter.client.models.ReadContextRuleFactsInnerArgumentsInner(/, **data: Any)#

Bases: pydantic.BaseModel

ReadContextRuleFactsInnerArgumentsInner

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.

source: pydantic.StrictStr | None#

key: pydantic.StrictStr | None#

value: pydantic.StrictStr | None#

model_config#

source_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of ReadContextRuleFactsInnerArgumentsInner from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of ReadContextRuleFactsInnerArgumentsInner from a dict

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

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#

class antimatter.client.models.ReadContextRuleMatchExpressionsInner(/, **data: Any)#

Bases: pydantic.BaseModel

ReadContextRuleMatchExpressionsInner

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.

source: pydantic.StrictStr#

key: pydantic.StrictStr#

operator: pydantic.StrictStr#

values: List[pydantic.StrictStr] | None#

value: pydantic.StrictStr | None#

model_config#

source_validate_enum(value)#: Validates the enum

operator_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of ReadContextRuleMatchExpressionsInner from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of ReadContextRuleMatchExpressionsInner from a dict

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

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#

class antimatter.client.models.ReadContextShortDetails(/, **data: Any)#

Bases: pydantic.BaseModel

Abridged details about a read context

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.

name: typing_extensions.Annotated[str, Field(strict=True)]#

summary: typing_extensions.Annotated[str, Field(strict=True, max_length=140)]#

description: typing_extensions.Annotated[str, Field(strict=True, max_length=4096)]#

disable_read_logging: pydantic.StrictBool | None#

key_cache_ttl: Optional[typing_extensions.Annotated[int, Field(strict=True, ge=0)]]#

read_parameters: List[antimatter.client.models.read_context_parameter.ReadContextParameter]#

imported: pydantic.StrictBool#

source_domain_id: Optional[typing_extensions.Annotated[str, Field(strict=True)]]#

source_domain_name: pydantic.StrictStr | None#

model_config#

name_validate_regular_expression(value)#: Validates the regular expression

source_domain_id_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of ReadContextShortDetails from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of ReadContextShortDetails from a dict

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

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#

class antimatter.client.models.ResourceExhaustedError(/, **data: Any)#

Bases: pydantic.BaseModel

Returned when the server is unable to process the request due to resource exhaustion or rate limiting

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.

resource_type: pydantic.StrictStr#

identifier: pydantic.StrictStr#

message: pydantic.StrictStr#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of ResourceExhaustedError from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of ResourceExhaustedError from a dict

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

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#

class antimatter.client.models.ResourceNotFoundError(/, **data: Any)#

Bases: pydantic.BaseModel

Returned when interacting with a valid URL, but the request references an unknown resource

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.

resource_type: pydantic.StrictStr#

identifier: pydantic.StrictStr#

message: pydantic.StrictStr#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of ResourceNotFoundError from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of ResourceNotFoundError from a dict

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

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#

class antimatter.client.models.RootEncryptionKeyIDResponse(/, **data: Any)#

Bases: pydantic.BaseModel

The newly created root encryption key’s ID.

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.

rek_id: pydantic.StrictStr#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of RootEncryptionKeyIDResponse from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of RootEncryptionKeyIDResponse from a dict

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

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#

class antimatter.client.models.RootEncryptionKeyItem(/, **data: Any)#

Bases: pydantic.BaseModel

RootEncryptionKeyItem

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.

source: pydantic.StrictStr#

var_resource_path: pydantic.StrictStr#

rek_id: pydantic.StrictStr#

description: pydantic.StrictStr#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of RootEncryptionKeyItem from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of RootEncryptionKeyItem from a dict

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

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#

class antimatter.client.models.RootEncryptionKeyTestResponse(/, **data: Any)#

Bases: pydantic.BaseModel

RootEncryptionKeyTestResponse

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.

id: typing_extensions.Annotated[str, Field(strict=True)]#

source: pydantic.StrictStr#

var_resource_path: pydantic.StrictStr#

description: pydantic.StrictStr#

status: pydantic.StrictStr#

status_message: pydantic.StrictStr#

latency_ms: pydantic.StrictFloat | pydantic.StrictInt#

model_config#

id_validate_regular_expression(value)#: Validates the regular expression

status_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of RootEncryptionKeyTestResponse from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of RootEncryptionKeyTestResponse from a dict

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

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#

class antimatter.client.models.RotateKeyEncryptionKeyResponse(/, **data: Any)#

Bases: pydantic.BaseModel

The results for a query of the capsule access log

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.

has_more: pydantic.StrictBool#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of RotateKeyEncryptionKeyResponse from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of RotateKeyEncryptionKeyResponse from a dict

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

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#

class antimatter.client.models.StarredDomainList(/, **data: Any)#

Bases: pydantic.BaseModel

StarredDomainList

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.

domains: List[typing_extensions.Annotated[str, Field(strict=True)]]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of StarredDomainList from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of StarredDomainList from a dict

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

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#

class antimatter.client.models.Tag(/, **data: Any)#

Bases: pydantic.BaseModel

Tag

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.

name: typing_extensions.Annotated[str, Field(strict=True, max_length=64)]#

value: typing_extensions.Annotated[str, Field(strict=True, max_length=256)]#

type: antimatter.client.models.tag_type_field.TagTypeField#

source: pydantic.StrictStr#

hook_version: Optional[typing_extensions.Annotated[str, Field(strict=True)]]#

model_config#

hook_version_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of Tag from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of Tag from a dict

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

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#

class antimatter.client.models.TagMeta(/, **data: Any)#

Bases: pydantic.BaseModel

TagMeta

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.

name: typing_extensions.Annotated[str, Field(strict=True, max_length=64)]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of TagMeta from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of TagMeta from a dict

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

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#

class antimatter.client.models.TagSet(/, **data: Any)#

Bases: pydantic.BaseModel

TagSet

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.

capsule_tags: List[antimatter.client.models.tag.Tag]#

span_tags: List[antimatter.client.models.tag_set_span_tags_inner.TagSetSpanTagsInner]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of TagSet from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of TagSet from a dict

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

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#

class antimatter.client.models.TagSetSpanTagsInner(/, **data: Any)#

Bases: pydantic.BaseModel

TagSetSpanTagsInner

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.

start: pydantic.StrictInt#

end: pydantic.StrictInt#

tags: List[antimatter.client.models.tag.Tag]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of TagSetSpanTagsInner from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of TagSetSpanTagsInner from a dict

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

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#

class antimatter.client.models.TagSummary(/, **data: Any)#

Bases: pydantic.BaseModel

TagSummary

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.

unique_tags: List[antimatter.client.models.tag_summary_unique_tags_inner.TagSummaryUniqueTagsInner]#

elided_tags: List[antimatter.client.models.tag_summary_elided_tags_inner.TagSummaryElidedTagsInner]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of TagSummary from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of TagSummary from a dict

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

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#

class antimatter.client.models.TagSummaryElidedTagsInner(/, **data: Any)#

Bases: pydantic.BaseModel

TagSummaryElidedTagsInner

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.

tag_name: pydantic.StrictStr#

num_unique_tags: pydantic.StrictInt#

total_occurrences: pydantic.StrictInt#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of TagSummaryElidedTagsInner from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of TagSummaryElidedTagsInner from a dict

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

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#

class antimatter.client.models.TagSummaryUniqueTagsInner(/, **data: Any)#

Bases: pydantic.BaseModel

TagSummaryUniqueTagsInner

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.

tag: antimatter.client.models.tag.Tag#

occurrences: pydantic.StrictInt#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of TagSummaryUniqueTagsInner from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of TagSummaryUniqueTagsInner from a dict

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

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#

class antimatter.client.models.TagTypeField#

Bases: str, enum.Enum

the type of this tag

STRING = 'string'#

NUMBER = 'number'#

BOOLEAN = 'boolean'#

DATE = 'date'#

UNARY = 'unary'#

classmethod from_json(json_str: str) → Self#: Create an instance of TagTypeField from a JSON string

capitalize()#

Return a capitalized version of the string.

More specifically, make the first character have upper case and the rest lower case.

casefold()#: Return a version of the string suitable for caseless comparisons.

center()#

Return a centered string of length width.

Padding is done using the specified fill character (default is a space).

count()#

S.count(sub[, start[, end]]) -> int

Return the number of non-overlapping occurrences of substring sub in string S[start:end]. Optional arguments start and end are interpreted as in slice notation.

encode()#

Encode the string using the codec registered for encoding.

encoding: The encoding in which to encode the string.
errors: The error handling scheme to use for encoding errors. The default is ‘strict’ meaning that encoding errors raise a UnicodeEncodeError. Other possible values are ‘ignore’, ‘replace’ and ‘xmlcharrefreplace’ as well as any other name registered with codecs.register_error that can handle UnicodeEncodeErrors.

endswith()#

S.endswith(suffix[, start[, end]]) -> bool

Return True if S ends with the specified suffix, False otherwise. With optional start, test S beginning at that position. With optional end, stop comparing S at that position. suffix can also be a tuple of strings to try.

expandtabs()#

Return a copy where all tab characters are expanded using spaces.

If tabsize is not given, a tab size of 8 characters is assumed.

find()#

S.find(sub[, start[, end]]) -> int

Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Return -1 on failure.

format()#

S.format(*args, **kwargs) -> str

Return a formatted version of S, using substitutions from args and kwargs. The substitutions are identified by braces (‘{’ and ‘}’).

format_map()#

S.format_map(mapping) -> str

Return a formatted version of S, using substitutions from mapping. The substitutions are identified by braces (‘{’ and ‘}’).

index()#

S.index(sub[, start[, end]]) -> int

Return the lowest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Raises ValueError when the substring is not found.

isalnum()#

Return True if the string is an alpha-numeric string, False otherwise.

A string is alpha-numeric if all characters in the string are alpha-numeric and there is at least one character in the string.

isalpha()#

Return True if the string is an alphabetic string, False otherwise.

A string is alphabetic if all characters in the string are alphabetic and there is at least one character in the string.

isascii()#

Return True if all characters in the string are ASCII, False otherwise.

ASCII characters have code points in the range U+0000-U+007F. Empty string is ASCII too.

isdecimal()#

Return True if the string is a decimal string, False otherwise.

A string is a decimal string if all characters in the string are decimal and there is at least one character in the string.

isdigit()#

Return True if the string is a digit string, False otherwise.

A string is a digit string if all characters in the string are digits and there is at least one character in the string.

isidentifier()#

Return True if the string is a valid Python identifier, False otherwise.

Call keyword.iskeyword(s) to test whether string s is a reserved identifier, such as “def” or “class”.

islower()#

Return True if the string is a lowercase string, False otherwise.

A string is lowercase if all cased characters in the string are lowercase and there is at least one cased character in the string.

isnumeric()#

Return True if the string is a numeric string, False otherwise.

A string is numeric if all characters in the string are numeric and there is at least one character in the string.

isprintable()#

Return True if the string is printable, False otherwise.

A string is printable if all of its characters are considered printable in repr() or if it is empty.

isspace()#

Return True if the string is a whitespace string, False otherwise.

A string is whitespace if all characters in the string are whitespace and there is at least one character in the string.

istitle()#

Return True if the string is a title-cased string, False otherwise.

In a title-cased string, upper- and title-case characters may only follow uncased characters and lowercase characters only cased ones.

isupper()#

Return True if the string is an uppercase string, False otherwise.

A string is uppercase if all cased characters in the string are uppercase and there is at least one cased character in the string.

join()#

Concatenate any number of strings.

The string whose method is called is inserted in between each given string. The result is returned as a new string.

Example: ‘.’.join([‘ab’, ‘pq’, ‘rs’]) -> ‘ab.pq.rs’

ljust()#

Return a left-justified string of length width.

Padding is done using the specified fill character (default is a space).

lower()#: Return a copy of the string converted to lowercase.

lstrip()#

Return a copy of the string with leading whitespace removed.

If chars is given and not None, remove characters in chars instead.

partition()#

Partition the string into three parts using the given separator.

This will search for the separator in the string. If the separator is found, returns a 3-tuple containing the part before the separator, the separator itself, and the part after it.

If the separator is not found, returns a 3-tuple containing the original string and two empty strings.

removeprefix()#

Return a str with the given prefix string removed if present.

If the string starts with the prefix string, return string[len(prefix):]. Otherwise, return a copy of the original string.

removesuffix()#

Return a str with the given suffix string removed if present.

If the string ends with the suffix string and that suffix is not empty, return string[:-len(suffix)]. Otherwise, return a copy of the original string.

replace()#

Return a copy with all occurrences of substring old replaced by new.

If the optional argument count is given, only the first count occurrences are replaced.

rfind()#

S.rfind(sub[, start[, end]]) -> int

Return the highest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Return -1 on failure.

rindex()#

S.rindex(sub[, start[, end]]) -> int

Return the highest index in S where substring sub is found, such that sub is contained within S[start:end]. Optional arguments start and end are interpreted as in slice notation.

Raises ValueError when the substring is not found.

rjust()#

Return a right-justified string of length width.

Padding is done using the specified fill character (default is a space).

rpartition()#

Partition the string into three parts using the given separator.

This will search for the separator in the string, starting at the end. If the separator is found, returns a 3-tuple containing the part before the separator, the separator itself, and the part after it.

If the separator is not found, returns a 3-tuple containing two empty strings and the original string.

rsplit()#

Return a list of the substrings in the string, using sep as the separator string.

Splitting starts at the end of the string and works to the front.

rstrip()#

Return a copy of the string with trailing whitespace removed.

If chars is given and not None, remove characters in chars instead.

split()#

Return a list of the substrings in the string, using sep as the separator string.

Note, str.split() is mainly useful for data that has been intentionally delimited. With natural text that includes punctuation, consider using the regular expression module.

splitlines()#

Return a list of the lines in the string, breaking at line boundaries.

Line breaks are not included in the resulting list unless keepends is given and true.

startswith()#

S.startswith(prefix[, start[, end]]) -> bool

Return True if S starts with the specified prefix, False otherwise. With optional start, test S beginning at that position. With optional end, stop comparing S at that position. prefix can also be a tuple of strings to try.

strip()#

Return a copy of the string with leading and trailing whitespace removed.

If chars is given and not None, remove characters in chars instead.

swapcase()#: Convert uppercase characters to lowercase and lowercase characters to uppercase.

title()#

Return a version of the string where each word is titlecased.

More specifically, words start with uppercased characters and all remaining cased characters have lower case.

translate()#

Replace each character in the string using the given translation table.

The table must implement lookup/indexing via __getitem__, for instance a dictionary or list. If this operation raises LookupError, the character is left untouched. Characters mapped to None are deleted.

upper()#: Return a copy of the string converted to uppercase.

zfill()#

Pad a numeric string with zeros on the left, to fill a field of the given width.

The string is never truncated.

name()#: The name of the Enum member.

value()#: The value of the Enum member.

class antimatter.client.models.UnauthorizedError(/, **data: Any)#

Bases: pydantic.BaseModel

Returned when the server cannot authorize the request

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.

message: pydantic.StrictStr#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of UnauthorizedError from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of UnauthorizedError from a dict

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

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#

class antimatter.client.models.UpsertSpanTagsRequest(/, **data: Any)#

Bases: pydantic.BaseModel

UpsertSpanTagsRequest

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.

summary: antimatter.client.models.tag_summary.TagSummary#

create_token: typing_extensions.Annotated[str, Field(min_length=64, strict=True)]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of UpsertSpanTagsRequest from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of UpsertSpanTagsRequest from a dict

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

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#

class antimatter.client.models.VerifyContactResponse(/, **data: Any)#

Bases: pydantic.BaseModel

Returned by successful contact email verification

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.

domain: typing_extensions.Annotated[str, Field(strict=True)]#

email: pydantic.StrictStr#

message: pydantic.StrictStr#

model_config#

domain_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of VerifyContactResponse from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of VerifyContactResponse from a dict

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

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#

class antimatter.client.models.WriteContextConfigInfo(/, **data: Any)#

Bases: pydantic.BaseModel

Information about write context config rules

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.

key_reuse_ttl: Optional[typing_extensions.Annotated[int, Field(strict=True, ge=0)]]#

required_hooks: List[antimatter.client.models.write_context_config_info_required_hooks_inner.WriteContextConfigInfoRequiredHooksInner]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of WriteContextConfigInfo from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of WriteContextConfigInfo from a dict

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

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#

class antimatter.client.models.WriteContextConfigInfoRequiredHooksInner(/, **data: Any)#

Bases: pydantic.BaseModel

WriteContextConfigInfoRequiredHooksInner

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.

hook: typing_extensions.Annotated[str, Field(strict=True)]#

constraint: typing_extensions.Annotated[str, Field(strict=True)]#

mode: pydantic.StrictStr#

model_config#

hook_validate_regular_expression(value)#: Validates the regular expression

constraint_validate_regular_expression(value)#: Validates the regular expression

mode_validate_enum(value)#: Validates the enum

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of WriteContextConfigInfoRequiredHooksInner from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of WriteContextConfigInfoRequiredHooksInner from a dict

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

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#

class antimatter.client.models.WriteContextDetails(/, **data: Any)#

Bases: pydantic.BaseModel

Details about a write context

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.

name: typing_extensions.Annotated[str, Field(strict=True)]#

summary: typing_extensions.Annotated[str, Field(strict=True, max_length=140)]#

description: typing_extensions.Annotated[str, Field(strict=True, max_length=4096)]#

config: antimatter.client.models.write_context_config_info.WriteContextConfigInfo#

imported: pydantic.StrictBool#

source_domain_id: Optional[typing_extensions.Annotated[str, Field(strict=True)]]#

source_domain_name: pydantic.StrictStr | None#

model_config#

name_validate_regular_expression(value)#: Validates the regular expression

source_domain_id_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of WriteContextDetails from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of WriteContextDetails from a dict

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

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#

class antimatter.client.models.WriteContextList(/, **data: Any)#

Bases: pydantic.BaseModel

A list of write contexts

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.

write_contexts: List[antimatter.client.models.write_context_details.WriteContextDetails]#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of WriteContextList from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of WriteContextList from a dict

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

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#

class antimatter.client.models.WriteContextRegexRule(/, **data: Any)#

Bases: pydantic.BaseModel

Regex classifier rule for a write context

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.

id: Optional[typing_extensions.Annotated[str, Field(strict=True)]]#

pattern: pydantic.StrictStr#

match_on_key: pydantic.StrictBool#

span_tags: List[antimatter.client.models.write_context_regex_tag.WriteContextRegexTag]#

capsule_tags: List[antimatter.client.models.write_context_regex_tag.WriteContextRegexTag]#

model_config#

id_validate_regular_expression(value)#: Validates the regular expression

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of WriteContextRegexRule from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of WriteContextRegexRule from a dict

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

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#

class antimatter.client.models.WriteContextRegexTag(/, **data: Any)#

Bases: pydantic.BaseModel

Tag descriptor for a write context regex rule

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.

name: typing_extensions.Annotated[str, Field(strict=True, max_length=64)]#

value: Optional[typing_extensions.Annotated[str, Field(strict=True, max_length=256)]]#

type: antimatter.client.models.tag_type_field.TagTypeField#

model_config#

to_str() → str#: Returns the string representation of the model using alias

to_json() → str#: Returns the JSON representation of the model using alias

classmethod from_json(json_str: str) → Self#: Create an instance of WriteContextRegexTag from a JSON string

to_dict() → Dict[str, Any]#

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic’s self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

classmethod from_dict(obj: Dict) → Self#: Create an instance of WriteContextRegexTag from a dict

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

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#

`APIKeyDomainIdentityProviderDetails`	Detailed information about an API key identity provider
`AWSServiceAccountKeyInfo`	The AWS service account information and details required to use the provided AWS hosted encryption keys for cryptographic operations.
`AccessLogEntry`	An individual capsule data-plane log entry. If adding a new read log entry, the session should be omitted (the server will fill it in)
`AccessLogEntryCreateInfo`	information available if the operation is of type "create".
`AccessLogEntryOpenInfo`	information available if the operation is of type "open".
`AccessLogEntryReadInfo`	information available if the operation is of type "read". allowedTags are those that were allowed without transformation during the read. redactedTags are those that were redacted during the read. tokenizedTags are those that were tokenized during the read.
`AccessLogResults`	The results for a query of the capsule access log
`ActiveRootEncryptionKeyID`	The stored key ID to use as the active root encryption key.
`AddCapsuleLogEntryRequest`	A request to add a capsule log entry
`AddReadContext`	A request to add read contexts
`AddWriteContext`	Information for adding/updating a write context
`AntimatterDelegatedAWSKeyInfo`	The details required to use an AWS KMS root encryption key that has been delegated to Antimatter's AWS account. This will use Antimatter's service account during set up of the AWS client.
`AvailableDelegatedRootEncryptionKeyProvider`	AvailableDelegatedRootEncryptionKeyProvider
`AvailableRootEncryptionKeyProviders`	AvailableRootEncryptionKeyProviders
`AvailableRootEncryptionKeyProvidersProvidersInner`	AvailableRootEncryptionKeyProvidersProvidersInner
`AvailableServiceAccountRootEncryptionKeyProvider`	AvailableServiceAccountRootEncryptionKeyProvider
`Capability`	A capability is attached to authenticated domain identities by an identity provider, and confers additional permissions upon the identity. This is done by writing domain policy rules that reference the capability.
`CapabilityDefinition`	A capability is attached to authenticated domain identities by an identity provider, and confers additional permissions upon the identity. This is done by writing domain policy rules that reference the capability.
`CapabilityDefinitionList`	A list of capability definitions
`CapabilityList`	A list of capabilities
`CapabilityRule`	A rule that refers to a domain identity capability. These rules are ANDed together
`CapabilityRuleMatchExpressionsInner`	CapabilityRuleMatchExpressionsInner
`CapsuleCreateResponse`	The response for the creation of a new capsule
`CapsuleInfo`	A summary of the capsule
`CapsuleList`	List of capsules
`CapsuleOpenRequest`	A request to open (decrypt) a capsule
`CapsuleOpenResponse`	Contains key material for a capsule
`CapsuleOpenResponseReadContextConfiguration`	the material required for enacting read context configuration (e.g. wasm stuff)
`CapsuleSealRequest`	Information applied when sealing a capsule (marking it as complete)
`ConflictError`	Returned when attempting to delete a resource that is still in use by other resources
`CreatePeerDomain`	Configuration options for creating a new subdomain.
`DataTaggingHookInput`	A request to classify PII in a batch of records
`DataTaggingHookInputRecordsInner`	DataTaggingHookInputRecordsInner
`DataTaggingHookInputRecordsInnerElementsInner`	DataTaggingHookInputRecordsInnerElementsInner
`DataTaggingHookResponse`	A response from invoking a data tagging hook
`DataTaggingHookResponseRecordsInner`	DataTaggingHookResponseRecordsInner
`DeleteTags`	DeleteTags
`Domain`	Information about a domain
`DomainAddReadContextRule200Response`	DomainAddReadContextRule200Response
`DomainAuthenticate`	An object containing external credentials that can be transmuted into a domain identity token
`DomainAuthenticateResponse`	A domain identity token
`DomainContactIssueVerifyRequest`	Parameters to request new validation request
`DomainControlLogEntry`	Results for a domain control log query
`DomainControlLogResults`	The results for a query of the capsule access log
`DomainFactList`	A list of defined fact types in the domain
`DomainHooksList`	A list of available hooks in this domain
`DomainHooksListHooksInner`	DomainHooksListHooksInner
`DomainIdentityAPIKeyPrincipalParams`	Details for an API key principal
`DomainIdentityEmailPrincipalParams`	Details for an email principal
`DomainIdentityHostedDomainPrincipalParams`	Additional details for a hosted domain principal
`DomainIdentityPrincipalDetails`	DomainIdentityPrincipalDetails
`DomainIdentityProviderDetails`	DomainIdentityProviderDetails
`DomainIdentityProviderInfo`	Information about an identity provider. This may be an imported provider or a provider in this domain
`DomainIdentityProviderList`	A list of identity providers
`DomainIdentityProviderPrincipalList`	A list of principals in an identity provider
`DomainIdentityProviderPrincipalParams`	Details to create a domain identity principal
`DomainIdentityProviderPrincipalType`	Principal type supported by an identity provider
`DomainIdentityProviderType`	Type of the identity provider.
`DomainInsertIdentityProviderPrincipal200Response`	DomainInsertIdentityProviderPrincipal200Response
`DomainInsertWriteContextRegexRule200Response`	DomainInsertWriteContextRegexRule200Response
`DomainPeerConfig`	Configuration of a domain peer. If the import alias is absent, the domain ID, without the initial "dm-" prefix, will be used
`DomainPeerList`	Information about the domains that this domain is peered with
`DomainPeerListPeersInner`	DomainPeerListPeersInner
`DomainPolicy`	A domain's policy. These rules govern who can view, edit or use which parts of a domain's configuration. Rules are executed in order of ascending priority number, and the execution stops with the first matching rule. If no rules match, the default action is 'deny'. If domain edit policy rules are imported from other domains in the peering configuration, the rules in those domains are independently evaluated to yield an allow/deny result and the final result from every domain, including this one, will be ANDed together. Thus, a deny in any domain yields an overall deny, and allow is only returned if all domains return allow.
`DomainPolicyRule`	A rule governing the domain's policy. All domain identity capabilities must match (AND) for the action to take effect. If the domainIdentity or facts sections are omitted, they match all domain identities and any fact configurations respectively. When updating or creating a rule, the id field may be omitted.
`DomainPrivateInfo`	Private information about a domain
`DomainPublicInfo`	Public information about a domain
`DomainResourceSummary`	A list of the resources and permissions available
`DomainResourceSummarySchemaInner`	DomainResourceSummarySchemaInner
`DomainSettings`	Additional configuration options for a domain
`DomainSettingsDisasterRecovery`	DomainSettingsDisasterRecovery
`DomainSettingsPatch`	A JSON patch to apply to the domain settings
`DomainStatus`	Information about the status of the domain
`DomainStatusNotificationsInner`	DomainStatusNotificationsInner
`DomainTagInfoResults`	Ordered list of the top 100 tags.
`Error`	An internal error
`Fact`	A fact is a piece of auxiliary information that can be used as part of an authorization policy. They are usually expressed as a statement such as has_role(principal, role_name)
`FactList`	A list of facts
`FactPolicyRulesInner`	FactPolicyRulesInner
`FactPolicyRulesInnerArgumentsInner`	FactPolicyRulesInnerArgumentsInner
`FactTypeDefinition`	A type definition (schema) for a fact
`GCPServiceAccountKeyInfo`	The GCP service account information and details required to use the provided GCP hosted encryption key for cryptographic operations.
`GoogleOAuthDomainIdentityProviderDetails`	Detailed information about a Google OAuth identity provider. If the clientID is omitted, an Antimatter Client ID will be used.
`HookInvocation`	The name and version of a hook that has been invoked on a capsule.
`InvalidRequestError`	Returned when one of the identifiers or arguments in the request is invalid
`JSONPatchRequestAdd`	JSONPatchRequestAdd
`JSONPatchRequestAddValue`	The value to add.
`JSONPatchRequestCopy`	JSONPatchRequestCopy
`JSONPatchRequestMove`	JSONPatchRequestMove
`JSONPatchRequestRemove`	JSONPatchRequestRemove
`JSONPatchRequestReplace`	JSONPatchRequestReplace
`JSONPatchRequestReplaceValue`	The value to replace.
`JSONPatchRequestTst`	JSONPatchRequestTst
`JSONPatchRequestTstValue`	The value to test.
`KeyInfos`	Holds the required service account information for varying providers.
`KeyInfosKeyInformation`	KeyInfosKeyInformation
`NewAccessLogEntry`	An individual capsule data-plane log entry, in the form required when inserting a new record
`NewAccessLogEntryReadInfo`	information available if the operation is of type "read". allowedTags are those that were allowed without transformation during the read. redactedTags are those that were redacted during the read. tokenizedTags are those that were tokenized during the read.
`NewCapabilityDefinition`	A capability is attached to authenticated domain identities by an identity provider, and confers additional permissions upon the identity. This is done by writing domain policy rules that reference the capability.
`NewDomain`	Parameters when creating a domain
`NewDomainResponse`	Information returned from a successful domain create request
`NewFact`	A fact is a piece of auxiliary information that can be used as part of an authorization policy. They are usually expressed as a statement such as has_role(principal, role_name)
`NewFactTypeDefinition`	A type definition (schema) for a fact being created
`NewFactTypeDefinitionArgumentsInner`	NewFactTypeDefinitionArgumentsInner
`NewReadContextConfigRule`	Information about what must be done to data when it is read from a capsule
`PatchRequestInner`	PatchRequestInner
`PrincipalInfo`	Detailed information about a principal
`PrincipalSummary`	PrincipalSummary
`ReadContextConfigRule`	Information about what must be done to data when it is read from a capsule
`ReadContextDetails`	Details about a read context
`ReadContextList`	A list of read contexts
`ReadContextParameter`	Declare parameters that can be passed in for use in read context configuration rules. It is expected that these are used for distinguishing who a read is being done on behalf of, and important attributes about that user (team, project, org etc).
`ReadContextRequiredHook`	ReadContextRequiredHook
`ReadContextRuleFactsInner`	ReadContextRuleFactsInner
`ReadContextRuleFactsInnerArgumentsInner`	ReadContextRuleFactsInnerArgumentsInner
`ReadContextRuleMatchExpressionsInner`	ReadContextRuleMatchExpressionsInner
`ReadContextShortDetails`	Abridged details about a read context
`ResourceExhaustedError`	Returned when the server is unable to process the request due to resource exhaustion or rate limiting
`ResourceNotFoundError`	Returned when interacting with a valid URL, but the request references an unknown resource
`RootEncryptionKeyIDResponse`	The newly created root encryption key's ID.
`RootEncryptionKeyItem`	RootEncryptionKeyItem
`RootEncryptionKeyTestResponse`	RootEncryptionKeyTestResponse
`RotateKeyEncryptionKeyResponse`	The results for a query of the capsule access log
`StarredDomainList`	StarredDomainList
`Tag`	Tag
`TagMeta`	TagMeta
`TagSet`	TagSet
`TagSetSpanTagsInner`	TagSetSpanTagsInner
`TagSummary`	TagSummary
`TagSummaryElidedTagsInner`	TagSummaryElidedTagsInner
`TagSummaryUniqueTagsInner`	TagSummaryUniqueTagsInner
`TagTypeField`	the type of this tag
`UnauthorizedError`	Returned when the server cannot authorize the request
`UpsertSpanTagsRequest`	UpsertSpanTagsRequest
`VerifyContactResponse`	Returned by successful contact email verification
`WriteContextConfigInfo`	Information about write context config rules
`WriteContextConfigInfoRequiredHooksInner`	WriteContextConfigInfoRequiredHooksInner
`WriteContextDetails`	Details about a write context
`WriteContextList`	A list of write contexts
`WriteContextRegexRule`	Regex classifier rule for a write context
`WriteContextRegexTag`	Tag descriptor for a write context regex rule

`antimatter.client.models`#

Submodules#

Package Contents#

Classes#

antimatter.client.models#

Submodules#

Package Contents#

Classes#

`antimatter.client.models`#