                       self.__class__.__name__, mask_type, output_size, predicted_available,
                       configfile, config)
          self.config = self.set_config(configfile, config)
          logger.debug("config: %s", self.config)
          self.mask_type = self.get_mask_type(mask_type, predicted_available)
          self.dummy = np.zeros((output_size, output_size, 3), dtype='float32')
  
          self.skip = self.config.get("type", None) is None
          logger.debug("Initialized %s", self.__class__.__name__)
  
      def set_config(self, configfile, config):
          """ Set the config to either global config or passed in config """
          section = ".".join(self.__module__.split(".")[-2:])
 
</s> remove                      "predicted_available: %s, configfile: %s, config: %s)",
                     self.__class__.__name__, mask_type, output_size, predicted_available,
                     configfile, config)
        self.config = self.set_config(configfile, config)
 </s> add                      "configfile: %s, config: %s)", self.__class__.__name__, mask_type,
                     output_size, configfile, config)
        self.config = self._set_config(configfile, config) </s> remove     def set_config(self, configfile, config):
        """ Set the config to either global config or passed in config """
 </s> add     @property
    def dummy(self):
        """:class:`numpy.ndarray`: A dummy mask of all zeros of the shape:
        (:attr:`output_size`, :attr:`output_size`, `3`)
        """
        return self._dummy

    @property
    def skip(self):
        """bool: ``True`` if the blur type config attribute is ``None`` otherwise ``False`` """
        return self.config.get("type", None) is None

    def _set_config(self, configfile, config):
        """ Set the correct configuration for the plugin based on whether a config file
        or pre-loaded config has been passed in.

        Parameters
        ----------
        configfile: str
            Location of custom configuration ``ini`` file. If ``None`` then use the
            default config location
        config: :class:`lib.config.FaceswapConfig`
            Pre-loaded :class:`lib.config.FaceswapConfig`. If passed, then this will be
            used over any configuration on disk. If ``None`` then it is ignored.

        Returns
        -------
        dict
            The configuration in dictionary form for the given from
            :attr:`lib.config.FaceswapConfig.config_dict`
        """ </s> remove     """ Parent class for adjustments """
    def __init__(self, mask_type, output_size, predicted_available, configfile=None, config=None):
 </s> add     """ Parent class for Mask Adjustment Plugins.

    All mask plugins must inherit from this class.

    Parameters
    ----------
    mask_type: str
        The type of mask that this plugin is being used for
    output_size: int
        The size, in pixels, of the output from the Faceswap model.
    configfile: str, Optional
        Optional location of custom configuration ``ini`` file. If ``None`` then use the default
        config location. Default: ``None``
    config: :class:`lib.config.FaceswapConfig`, Optional
        Optional pre-loaded :class:`lib.config.FaceswapConfig`. If passed, then this will be used
        over any configuration on disk. If ``None`` then it is ignored. Default: ``None``


    Attributes
    ----------
    config: dict
        The configuration dictionary for this plugin.
    mask_type: str
        The type of mask that this plugin is being used for.
    """
    def __init__(self, mask_type, output_size, configfile=None, config=None): </s> remove     """ Return the requested mask """
    def __init__(self, mask_type, output_size, predicted_available, **kwargs):
        super().__init__(mask_type, output_size, predicted_available, **kwargs)
        self.do_erode = self.config.get("erosion", 0) != 0
        self.do_blend = self.config.get("type", None) is not None

    def process(self, detected_face, predicted_mask=None):
        """ Return mask and perform processing """
        mask = self.get_mask(detected_face, predicted_mask)
 </s> add     """ Manipulations to perform to the mask that is to be applied to the output of the Faceswap
    model.

    Parameters
    ----------
    mask_type: str
        The mask type to use for this plugin
    output_size: int
        The size of the output from the Faceswap model.
    coverage_ratio: float
        The coverage ratio that the Faceswap model was trained at.
    **kwargs: dict, optional
        See the parent :class:`~plugins.convert.mask._base` for additional keyword arguments.
    """
    def __init__(self, mask_type, output_size, coverage_ratio, **kwargs):
        super().__init__(mask_type, output_size, **kwargs)
        self._do_erode = self.config.get("erosion", 0) != 0
        self._coverage_ratio = coverage_ratio

    def process(self, detected_face, predicted_mask=None):  # pylint:disable=arguments-differ
        """ Obtain the requested mask type and perform any defined mask manipulations.

        Parameters
        ----------
        detected_face: :class:`lib.faces_detect.DetectedFace`
            The DetectedFace object as returned from :class:`scripts.convert.Predictor`.
        predicted_mask: :class:`numpy.ndarray`, optional
            The predicted mask as output from the Faceswap Model, if the model was trained
            with a mask, otherwise ``None``. Default: ``None``.

        Returns
        -------
        mask: :class:`numpy.ndarray`
            The mask with all requested manipulations applied
        raw_mask: :class:`numpy.ndarray`
            The mask with no erosion/dilation applied
        """
        mask = self._get_mask(detected_face, predicted_mask) </s> remove         self.config = Config(None)
        self.config_dicts = self.get_config_dicts()  # Holds currently saved config
 </s> add         self._config = Config(None)
        self.mask_type = mask_type
        self._dummy = np.zeros((output_size, output_size, 3), dtype='float32')
                       self.__class__.__name__, mask_type, output_size, predicted_available,
                       configfile, config)
          self.config = self.set_config(configfile, config)
          logger.debug("config: %s", self.config)
        self.mask_type = mask_type
        self._dummy = np.zeros((output_size, output_size, 3), dtype='float32')
        self.mask_type = mask_type
        self._dummy = np.zeros((output_size, output_size, 3), dtype='float32')
        self.mask_type = mask_type
        self._dummy = np.zeros((output_size, output_size, 3), dtype='float32')
        self.mask_type = mask_type
        self._dummy = np.zeros((output_size, output_size, 3), dtype='float32')
          logger.debug("Initialized %s", self.__class__.__name__)
  
      def set_config(self, configfile, config):
          """ Set the config to either global config or passed in config """
          section = ".".join(self.__module__.split(".")[-2:])
keep keep keep keep replace replace replace replace keep keep keep keep keep
 Smart Masks to Convert (#957)

- scripts.convert - Use Smart Masks for Convert
    * Make on-the-fly conversion an explicit option

- Move BlurMask to lib.faces_detect

- tools.preview - Fix for smart masks
    * Subclass from tk.Tk
    * Options to lib.gui.control_helper
    *variable cleanup

- lib.logger - Demote more tensorflow deprecation messages

- Documentation:
    * lib.faces_detect.BlurMask
    * plugins.convert.mask
    * lib.convert
    * scripts.convert
    * scripts.fsmedia
    * tools.preview 
plugins/convert/mask/_base.py