qat.core.config.descriptions module

class ClassDescription(**data)

Bases: NoExtraFieldsModel, Generic[T]

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

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

self is explicitly positional-only to allow self as a field name.

config: dict

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'ser_json_inf_nan': 'constants', 'use_enum_values': False, 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

partial()

Returns a partially configured class

type: TypeVar(T)

class ClassDescription(**data)

Bases: NoExtraFieldsModel, Generic[T]

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

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

self is explicitly positional-only to allow self as a field name.

config: dict

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'ser_json_inf_nan': 'constants', 'use_enum_values': False, 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

partial()

Returns a partially configured class

type: TypeVar(T)

class HardwareLoaderDescription(**data)

Bases: NoExtraFieldsModel

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

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

self is explicitly positional-only to allow self as a field name.

config: dict

construct()

Returns the described Hardware Loader

Return type:

BaseModelLoader

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'ser_json_inf_nan': 'constants', 'use_enum_values': False, 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str

type: Annotated[ImportString]

class PipelineClassDescription(**data)

Bases: NoExtraFieldsModel

Allows pipelines to be specified in a granular way and constructed as an updateable pipeline, allowing the hardware model to be refreshed.

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

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

self is explicitly positional-only to allow self as a field name.

backend: Union[Annotated[ImportString], Annotated[ClassDescription[Annotated[ImportString, AfterValidator]]]]

config: dict

construct(loader)

Constructs and returns a Pipeline from its description

Parameters:

model – The instantiated hardware model, this is required to be the model provided by the associated hardware_loader

Return type:

ConfigurablePipeline

Returns:

The pipeline as a ConfigurablePipeline instance.

default: bool

engine: Union[Annotated[ImportString], Annotated[ClassDescription[Annotated[ImportString, AfterValidator]]]]

frontend: Union[Annotated[ImportString], Annotated[ClassDescription[Annotated[ImportString, AfterValidator]]]]

hardware_loader: str | None

middleend: Union[Annotated[ImportString], Annotated[ClassDescription[Annotated[ImportString, AfterValidator]]]]

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'ser_json_inf_nan': 'constants', 'use_enum_values': False, 'validate_assignment': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str

results_pipeline: Union[Annotated[ImportString], Annotated[ClassDescription[Annotated[ImportString, AfterValidator]]]]

runtime: Union[Annotated[ImportString], Annotated[ClassDescription[Annotated[ImportString, AfterValidator]]]]

target_data: Union[Annotated[ImportString], Annotated[ClassDescription[Annotated[ImportString, AfterValidator]]]]

class PipelineFactoryDescription(**data)

Bases: NoExtraFieldsModel

A description pointing to a function that produces a pipeline, which is configured by a model, target data, an engine, and custom configuration.

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

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

self is explicitly positional-only to allow self as a field name.

config: dict

construct(loader)

Constructs and returns a Pipeline from its description

Parameters:

model – The instantiated hardware model, this is required to be the model provided by the associated hardware_loader

Return type:

PipelineFactory

Returns:

The constructed pipeline

default: bool

engine: Union[Annotated[ImportString], Annotated[ClassDescription[Annotated[ImportString, AfterValidator]]], None]

hardware_loader: str | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'ser_json_inf_nan': 'constants', 'use_enum_values': False, 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str

pipeline: Annotated[ImportString]

target_data: Optional[Annotated[ImportString]]

class PipelineInstanceDescription(**data)

Bases: NoExtraFieldsModel

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

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

self is explicitly positional-only to allow self as a field name.

construct()

Returns the requested Pipeline instance

Return type:

Pipeline

default: bool

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'ser_json_inf_nan': 'constants', 'use_enum_values': False, 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str

pipeline: Annotated[ImportString]

class UpdateablePipelineDescription(**data)

Bases: NoExtraFieldsModel

A description pointing to an updateable pipeline, which can be configured with a custom hardware model (loader), target data, and an engine. It also always custom configuration (given by the concrete updateable pipeline class).

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

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

self is explicitly positional-only to allow self as a field name.

config: dict

construct(loader)

Constructs and returns a Pipeline from its description

Parameters:

loader (BaseModelLoader) – The hardware model loader to fetch the hardware model.

Return type:

UpdateablePipeline

Returns:

The updateable pipeline with a constructed pipeline instance.

default: bool

engine: Union[Annotated[ImportString], Annotated[ClassDescription[Annotated[ImportString, AfterValidator]]], None]

hardware_loader: str | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'ser_json_inf_nan': 'constants', 'use_enum_values': False, 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str

pipeline: Annotated[ImportString]

target_data: Optional[Annotated[ImportString]]