abracudabra._validate
=====================

.. py:module:: abracudabra._validate


Attributes
----------

.. autoapisummary::

   abracudabra._validate.LIBRARY_TO_CONCRETE_TYPES


Classes
-------

.. autoapisummary::

   abracudabra._validate.Library


Functions
---------

.. autoapisummary::

   abracudabra._validate._get_numpy_type
   abracudabra._validate._get_cupy_type
   abracudabra._validate._get_torch_type
   abracudabra._validate._get_pandas_type
   abracudabra._validate._get_cudf_type
   abracudabra._validate._get_concrete_types
   abracudabra._validate.validate_obj_type


Module Contents
---------------

.. py:function:: _get_numpy_type() -> dict[str, type]

.. py:function:: _get_cupy_type() -> dict[str, type]

.. py:function:: _get_torch_type() -> dict[str, type]

.. py:function:: _get_pandas_type() -> dict[str, type]

.. py:function:: _get_cudf_type() -> dict[str, type]

.. py:class:: Library(*args, **kwds)

   Bases: :py:obj:`enum.Enum`


   Library names.


   .. py:attribute:: numpy
      :value: 'numpy'



   .. py:attribute:: cupy
      :value: 'cupy'



   .. py:attribute:: torch
      :value: 'torch'



   .. py:attribute:: pandas
      :value: 'pandas'



   .. py:attribute:: cudf
      :value: 'cudf'



.. py:data:: LIBRARY_TO_CONCRETE_TYPES
   :type:  dict[Library, collections.abc.Callable[[], collections.abc.Mapping[str, type]]]

   Mapping from library names to functions that return a mapping of concrete types.

.. py:function:: _get_concrete_types(library: Library, types: str | collections.abc.Iterable[str] | None = None) -> type | tuple[type, Ellipsis]

.. py:function:: validate_obj_type(obj: object, /, library: Literal[Library], types: str | collections.abc.Iterable[str] | None = ...) -> TypeGuard[numpy.ndarray]
                 validate_obj_type(obj: object, /, library: Literal[Library], types: str | collections.abc.Iterable[str] | None = ...) -> TypeGuard[Any]
                 validate_obj_type(obj: object, /, library: Literal[Library], types: str | collections.abc.Iterable[str] | None = ...) -> TypeGuard[torch.Tensor]
                 validate_obj_type(obj: object, /, library: Literal[Library], types: str | collections.abc.Iterable[str] | None = ...) -> TypeGuard[pandas.Index | pandas.Series[Any] | pandas.DataFrame]
                 validate_obj_type(obj: object, /, library: Literal[Library], types: str | collections.abc.Iterable[str] | None = ...) -> TypeGuard[cudf.Index | cudf.Series | cudf.DataFrame]
                 validate_obj_type(obj: object, /, library: Library, types: str | collections.abc.Iterable[str] | None = ...) -> bool

   Validate an object ensuring it matches the type from a specified library.

   :param obj: The object to validate.
   :param library: Library name from Enum 'Library'.
   :param types: The type(s) to validate against.
                 For example, ``'array'``, ``'index'``, ``'series'``,
                 ``'dataframe'``, ``'tensor'``.

   :returns: The input object, if validation succeeds.

   :raises ValueError: If an unsupported library is provided.
   :raises TypeError: If the object type doesn't match the expected type.