Class swarmauri_base.tensors.TensorBase.TensorBase

swarmauri_base.tensors.TensorBase.TensorBase

Bases: ITensor, ComponentBase

Base implementation of the ITensor interface.

This class provides a foundation for tensor operations including reshaping, contraction, and broadcasting. It implements the abstract methods defined in ITensor but raises NotImplementedError, serving as a template for concrete tensor implementations.

Attributes

resource : Optional[str] Resource type identifier, defaults to TENSOR

resource class-attribute instance-attribute

resource = TENSOR.value

shape property

shape

Get the shape of the tensor.

Returns

Shape The dimensions of the tensor

Raises

NotImplementedError Method must be implemented by subclasses

dtype property

dtype

Get the data type of the tensor elements.

Returns

type The data type of the elements

Raises

NotImplementedError Method must be implemented by subclasses

type class-attribute instance-attribute

type = 'ComponentBase'

model_config class-attribute instance-attribute

model_config = ConfigDict(
    extra="allow", arbitrary_types_allowed=True
)

id class-attribute instance-attribute

id = Field(default_factory=generate_id)

members class-attribute instance-attribute

members = None

owners class-attribute instance-attribute

owners = None

host class-attribute instance-attribute

host = None

default_logger class-attribute

default_logger = None

logger class-attribute instance-attribute

logger = None

name class-attribute instance-attribute

name = None

version class-attribute instance-attribute

version = '0.1.0'

reshape

reshape(shape)

Reshape the tensor to the specified dimensions.

Parameters

shape : Shape The new dimensions

Returns

ITensor A reshaped tensor

Raises

NotImplementedError Method must be implemented by subclasses

Source code in swarmauri_base/tensors/TensorBase.py

def reshape(self, shape: Shape) -> "ITensor":
    """
    Reshape the tensor to the specified dimensions.

    Parameters
    ----------
    shape : Shape
        The new dimensions

    Returns
    -------
    ITensor
        A reshaped tensor

    Raises
    ------
    NotImplementedError
        Method must be implemented by subclasses
    """
    raise NotImplementedError("reshape method must be implemented by subclasses")

tolist

tolist()

Convert the tensor to a nested list.

Returns

List A nested list representing the tensor

Raises

NotImplementedError Method must be implemented by subclasses

Source code in swarmauri_base/tensors/TensorBase.py

def tolist(self) -> List:
    """
    Convert the tensor to a nested list.

    Returns
    -------
    List
        A nested list representing the tensor

    Raises
    ------
    NotImplementedError
        Method must be implemented by subclasses
    """
    raise NotImplementedError("tolist method must be implemented by subclasses")

transpose

transpose(axes=None)

Transpose (permute) the tensor's axes.

Parameters

axes : Union[None, Tuple[int, ...]], optional The new order of dimensions. If None, reverse the dimensions.

Returns

ITensor The transposed tensor

Raises

NotImplementedError Method must be implemented by subclasses

Source code in swarmauri_base/tensors/TensorBase.py

def transpose(self, axes: Union[None, Tuple[int, ...]] = None) -> "ITensor":
    """
    Transpose (permute) the tensor's axes.

    Parameters
    ----------
    axes : Union[None, Tuple[int, ...]], optional
        The new order of dimensions. If None, reverse the dimensions.

    Returns
    -------
    ITensor
        The transposed tensor

    Raises
    ------
    NotImplementedError
        Method must be implemented by subclasses
    """
    raise NotImplementedError("transpose method must be implemented by subclasses")

broadcast

broadcast(shape)

Broadcast the tensor to a new shape.

Parameters

shape : Shape The shape to broadcast to

Returns

ITensor The broadcasted tensor

Raises

NotImplementedError Method must be implemented by subclasses

Source code in swarmauri_base/tensors/TensorBase.py

def broadcast(self, shape: Shape) -> "ITensor":
    """
    Broadcast the tensor to a new shape.

    Parameters
    ----------
    shape : Shape
        The shape to broadcast to

    Returns
    -------
    ITensor
        The broadcasted tensor

    Raises
    ------
    NotImplementedError
        Method must be implemented by subclasses
    """
    raise NotImplementedError("broadcast method must be implemented by subclasses")

astype

astype(dtype)

Cast the tensor to a specified data type.

Parameters

dtype : type The target data type

Returns

ITensor A new tensor with the specified data type

Raises

NotImplementedError Method must be implemented by subclasses

Source code in swarmauri_base/tensors/TensorBase.py

def astype(self, dtype: type) -> "ITensor":
    """
    Cast the tensor to a specified data type.

    Parameters
    ----------
    dtype : type
        The target data type

    Returns
    -------
    ITensor
        A new tensor with the specified data type

    Raises
    ------
    NotImplementedError
        Method must be implemented by subclasses
    """
    raise NotImplementedError("astype method must be implemented by subclasses")

to_matrix

to_matrix()

Convert the tensor to a matrix if possible.

Returns

IMatrix The tensor as a matrix

Raises

NotImplementedError Method must be implemented by subclasses

Source code in swarmauri_base/tensors/TensorBase.py

def to_matrix(self) -> Literal[IMatrix]:
    """
    Convert the tensor to a matrix if possible.

    Returns
    -------
    IMatrix
        The tensor as a matrix

    Raises
    ------
    NotImplementedError
        Method must be implemented by subclasses
    """
    raise NotImplementedError("to_matrix method must be implemented by subclasses")

to_vector

to_vector()

Convert the tensor to a vector if possible.

Returns

IVector The tensor as a vector

Raises

NotImplementedError Method must be implemented by subclasses

Source code in swarmauri_base/tensors/TensorBase.py

def to_vector(self) -> Literal[IVector]:
    """
    Convert the tensor to a vector if possible.

    Returns
    -------
    IVector
        The tensor as a vector

    Raises
    ------
    NotImplementedError
        Method must be implemented by subclasses
    """
    raise NotImplementedError("to_vector method must be implemented by subclasses")

register_model classmethod

register_model()

Decorator to register a base model in the unified registry.

RETURNS	DESCRIPTION
Callable	A decorator function that registers the model class. TYPE: Callable[[Type[BaseModel]], Type[BaseModel]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_model(cls) -> Callable[[Type[BaseModel]], Type[BaseModel]]:
    """
    Decorator to register a base model in the unified registry.

    Returns:
        Callable: A decorator function that registers the model class.
    """

    def decorator(model_cls: Type[BaseModel]):
        """Register ``model_cls`` as a base model."""
        model_name = model_cls.__name__
        if model_name in cls._registry:
            glogger.warning(
                "Model '%s' is already registered; skipping duplicate.", model_name
            )
            return model_cls

        cls._registry[model_name] = {"model_cls": model_cls, "subtypes": {}}
        glogger.debug("Registered base model '%s'.", model_name)
        DynamicBase._recreate_models()
        return model_cls

    return decorator

register_type classmethod

register_type(resource_type=None, type_name=None)

Decorator to register a subtype under one or more base models in the unified registry.

PARAMETER	DESCRIPTION
resource_type	The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase) are used. TYPE: Optional[Union[Type[T], List[Type[T]]]] DEFAULT: None
type_name	An optional custom type name for the subtype. TYPE: Optional[str] DEFAULT: None

RETURNS	DESCRIPTION
Callable	A decorator function that registers the subtype. TYPE: Callable[[Type[DynamicBase]], Type[DynamicBase]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_type(
    cls,
    resource_type: Optional[Union[Type[T], List[Type[T]]]] = None,
    type_name: Optional[str] = None,
) -> Callable[[Type["DynamicBase"]], Type["DynamicBase"]]:
    """
    Decorator to register a subtype under one or more base models in the unified registry.

    Parameters:
        resource_type (Optional[Union[Type[T], List[Type[T]]]]):
            The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase)
            are used.
        type_name (Optional[str]): An optional custom type name for the subtype.

    Returns:
        Callable: A decorator function that registers the subtype.
    """

    def decorator(subclass: Type["DynamicBase"]):
        """Register ``subclass`` as a subtype."""
        if resource_type is None:
            resource_types = [
                base for base in subclass.__bases__ if base is not cls
            ]
        elif not isinstance(resource_type, list):
            resource_types = [resource_type]
        else:
            resource_types = resource_type

        for rt in resource_types:
            if not issubclass(subclass, rt):
                raise TypeError(
                    f"'{subclass.__name__}' must be a subclass of '{rt.__name__}'."
                )
            final_type_name = type_name or getattr(
                subclass, "_type", subclass.__name__
            )
            base_model_name = rt.__name__

            if base_model_name not in cls._registry:
                cls._registry[base_model_name] = {"model_cls": rt, "subtypes": {}}
                glogger.debug(
                    "Created new registry entry for base model '%s'.",
                    base_model_name,
                )

            subtypes_dict = cls._registry[base_model_name]["subtypes"]
            if final_type_name in subtypes_dict:
                glogger.warning(
                    "Type '%s' already exists under '%s'; skipping duplicate.",
                    final_type_name,
                    base_model_name,
                )
                continue

            subtypes_dict[final_type_name] = subclass
            glogger.debug(
                "Registered '%s' as '%s' under '%s'.",
                subclass.__name__,
                final_type_name,
                base_model_name,
            )

        DynamicBase._recreate_models()
        return subclass

    return decorator

model_validate_toml classmethod

model_validate_toml(toml_data)

Validate a model from a TOML string.

Source code in swarmauri_base/TomlMixin.py

@classmethod
def model_validate_toml(cls, toml_data: str):
    """Validate a model from a TOML string."""
    try:
        # Parse TOML into a Python dictionary
        toml_content = tomllib.loads(toml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(toml_content))
    except tomllib.TOMLDecodeError as e:
        raise ValueError(f"Invalid TOML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_toml

model_dump_toml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a TOML representation of the model.

Source code in swarmauri_base/TomlMixin.py

def model_dump_toml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a TOML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into TOML
    return toml.dumps(filtered_data)

model_validate_yaml classmethod

model_validate_yaml(yaml_data)

Validate a model from a YAML string.

Source code in swarmauri_base/YamlMixin.py

@classmethod
def model_validate_yaml(cls, yaml_data: str):
    """Validate a model from a YAML string."""
    try:
        # Parse YAML into a Python dictionary
        yaml_content = yaml.safe_load(yaml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(yaml_content))
    except yaml.YAMLError as e:
        raise ValueError(f"Invalid YAML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_yaml

model_dump_yaml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a YAML representation of the model.

Source code in swarmauri_base/YamlMixin.py

def model_dump_yaml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a YAML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into YAML using safe mode
    return yaml.safe_dump(filtered_data, default_flow_style=False)

model_post_init

model_post_init(logger=None)

Assign a logger instance after model initialization.

Source code in swarmauri_base/LoggerMixin.py

def model_post_init(self, logger: Optional[FullUnion[LoggerBase]] = None) -> None:
    """Assign a logger instance after model initialization."""

    # Directly assign the provided FullUnion[LoggerBase] or fallback to the
    # class-level default.
    self.logger = self.logger or logger or self.default_logger