:py:mod:`medcat.utils.config_utils`
===================================

.. py:module:: medcat.utils.config_utils


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

Classes
~~~~~~~

.. autoapisummary::

   medcat.utils.config_utils.WAFCarrier



Functions
~~~~~~~~~

.. autoapisummary::

   medcat.utils.config_utils.is_old_type_config_dict
   medcat.utils.config_utils.fix_waf_lambda
   medcat.utils.config_utils.ensure_backward_compatibility
   medcat.utils.config_utils.get_and_del_weighted_average_from_config
   medcat.utils.config_utils.weighted_average
   medcat.utils.config_utils.default_weighted_average
   medcat.utils.config_utils.attempt_fix_weighted_average_function
   medcat.utils.config_utils._fix_waf



Attributes
~~~~~~~~~~

.. autoapisummary::

   medcat.utils.config_utils.logger


.. py:class:: WAFCarrier


   Bases: :py:obj:`Protocol`

   Base class for protocol classes.

   Protocol classes are defined as::

       class Proto(Protocol):
           def meth(self) -> int:
               ...

   Such classes are primarily used with static type checkers that recognize
   structural subtyping (static duck-typing), for example::

       class C:
           def meth(self) -> int:
               return 0

       def func(x: Proto) -> int:
           return x.meth()

       func(C())  # Passes static type check

   See PEP 544 for details. Protocol classes decorated with
   @typing.runtime_checkable act as simple-minded runtime protocols that check
   only the presence of given attributes, ignoring their type signatures.
   Protocol classes can be generic, they are defined as::

       class GenProto(Protocol[T]):
           def meth(self) -> T:
               ...

   .. py:property:: weighted_average_function
      :type: Callable[[float], int]



.. py:data:: logger

   

.. py:function:: is_old_type_config_dict(d)

   Checks if the dict provided is an old style (jsonpickle) config.

   This checks for json-pickle specific keys such as py/object and py/state.
   If both of those are keys somewhere within the 2 initial layers of the
   nested dict, it's considered old style.

   :param d: Loaded config.
   :type d: dict

   :Returns: **bool** -- Whether it's an old style (jsonpickle) config.


.. py:function:: fix_waf_lambda(carrier)


.. py:function:: ensure_backward_compatibility(config, workers)


.. py:function:: get_and_del_weighted_average_from_config(config)


.. py:function:: weighted_average(step, factor)


.. py:function:: default_weighted_average(step)


.. py:function:: attempt_fix_weighted_average_function(waf)

   Attempf fix weighted_average_function.

   When saving a model (dill.dump) in older python versions (3.10 and before)
   and then loading it back up with newer versions of python (3.11 and later)
   there can be an issue with loading `config.linking.weighted_average_function`.
   The value attributed to this is generally a `functools.partial`. And it loads
   just fine, but cannot subsequently be called.

   This method fixes the issue if the default function is used. It retains
   the arguments and keyword arguments of the original `partial.

   What this method does is recreate the partial function based on the
   arguments originally provided. Along with the fix, it logs a warning.

   However, if a non-default method is used, we are unable to fix it.
   That is because we do not know which method may have been used.
   In that case, a warning is logged.

   :param waf: The weighted average function.
   :type waf: Callable[[int], float]

   :Returns: **Callable[[int], float]** -- The (potentially) fixed function.


.. py:function:: _fix_waf(waf)