  msgstr ""
  
  #: plugins/train/_config.py:299
  msgid ""
  "[Not PlaidML], NVIDIA GPUs can run operations in float16 faster than in "
  "float32. Mixed precision allows you to use a mix of float16 with float32, to "
  "get the performance benefits from float16 and the numeric stability benefits "
  "from float32.\n"
  "\n"
  "This is untested on DirectML backend, but will run on most Nvidia models. it "
 
</s> remove "[Not PlaidML] Apply AutoClipping to the gradients. AutoClip analyzes the "
 </s> add "Apply AutoClipping to the gradients. AutoClip analyzes the " </s> remove         if get_backend() == "amd":  # Mixed precision not supported on amd
            return model

 </s> add  </s> remove         if self.backend == "amd" and sys.version_info >= (3, 9):
            logger.error("The AMD version of Faceswap cannot be installed on versions of Python "
                         "higher than 3.8")
            sys.exit(1)
 </s> add  </s> remove     fc_depth=dict(
        default=1,
        info="The number of consecutive Dense (fully connected) layers to include in each side's "
             "intermediate layer.",
        datatype=int,
        rounding=1,
        min_max=(0, 16),
        group="hidden layers",
        fixed=True),
    fc_min_filters=dict(
        default=1024,
        info="The number of filters to use for the initial fully connected layer. The number of "
             "nodes actually used is: fc_min_filters x fc_dimensions x fc_dimensions.\nNB: This "
             "value may be scaled down, depending on output resolution.",
        datatype=int,
        rounding=16,
        min_max=(16, 5120),
        group="hidden layers",
        fixed=True),
    fc_max_filters=dict(
        default=1024,
        info="This is the number of filters to be used in the final reshape layer at the end of "
             "the fully connected layers. The actual number of nodes used for the final fully "
             "connected layer is: fc_min_filters x fc_dimensions x fc_dimensions.\nNB: This value "
             "may be scaled down, depending on output resolution.",
        datatype=int,
        rounding=64,
        min_max=(128, 5120),
        group="hidden layers",
        fixed=True),
    fc_dimensions=dict(
        default=4,
        info="The height and width dimension for the final reshape layer at the end of the fully "
             "connected layers.\nNB: The total number of nodes within the final fully connected "
             "layer will be: fc_dimensions x fc_dimensions x fc_max_filters.",
        datatype=int,
        rounding=1,
        min_max=(1, 16),
        group="hidden layers",
        fixed=True),
    fc_filter_slope=dict(
        default=-0.5,
        info="The rate that the filters move from the minimum number of filters to the maximum "
             "number of filters. EG:\n"
             "Negative numbers will change the number of filters quicker at first and slow down "
             "each layer.\n"
             "Positive numbers will change the number of filters slower at first but then speed "
             "up each layer.\n"
             "0.0 - This will change at a linear rate (i.e. the same number of filters will be "
             "changed at each layer).",
        datatype=float,
        min_max=(-.99, .99),
        rounding=2,
        group="hidden layers",
        fixed=True),
    fc_dropout=dict(
        default=0.0,
        info="Dropout is a form of regularization that can prevent a model from over-fitting and "
             "help to keep neurons 'alive'. 0.5 will dropout half the connections between each "
             "fully connected layer, 0.25 will dropout a quarter of the connections etc. Set to "
             "0.0 to disable.",
        datatype=float,
        rounding=2,
        min_max=(0.0, 0.99),
        group="hidden layers",
        fixed=False),
    fc_upsampler=dict(
        default="upsample2d",
        info="The type of dimensional upsampling to perform at the end of the fully connected "
             "layers, if upsamples > 0. The number of filters used for the upscale layers will be "
             "the value given in 'fc_upsample_filters'."
             "\n\tupsample2d - A lightweight and VRAM friendly method. 'quick and dirty' but does "
             "not learn any parameters"
             "\n\tsubpixel - Sub-pixel upscaler using depth-to-space which may require more "
             "VRAM."
             "\n\tresize_images - Uses the Keras resize_image function to save about half as much "
             "vram as the heaviest methods."
             "\n\tupscale_fast - Developed by Andenixa. Focusses on speed to upscale, but "
             "requires more VRAM."
             "\n\tupscale_hybrid - Developed by Andenixa. Uses a combination of PixelShuffler and "
             "Upsampling2D to upscale, saving about 1/3rd of VRAM of the heaviest methods.",
        datatype=str,
        choices=["resize_images", "subpixel", "upscale_fast", "upscale_hybrid", "upsample2d"],
        group="hidden layers",
        gui_radio=False,
        fixed=True),
    fc_upsamples=dict(
        default=1,
        info="Some upsampling can occur within the Fully Connected layers rather than in the "
             "Decoder to increase the dimensional space. Set how many upscale layers should occur "
             "within the Fully Connected layers.",
        datatype=int,
        min_max=(0, 4),
        rounding=1,
        group="hidden layers",
        fixed=True),
    fc_upsample_filters=dict(
        default=512,
        info="If you have selected an upsampler which requires filters (i.e. any upsampler with "
             "the exception of Upsampling2D), then this is the number of filters to be used for "
             "the upsamplers within the fully connected layers,  NB: This value may be scaled "
             "down, depending on output resolution. Also note, that this figure will dictate the "
             "number of filters used for the G-Block, if selected.",
        datatype=int,
        rounding=64,
        min_max=(128, 5120),
        group="hidden layers",
        fixed=True),
 </s> add     "fc_depth": {
        "default": 1,
        "info": (
            "The number of consecutive Dense (fully connected) layers to include in each "
            "side's intermediate layer."),
        "datatype": int,
        "rounding": 1,
        "min_max": (0, 16),
        "group": "hidden layers",
        "fixed": True},
    "fc_min_filters": {
        "default": 1024,
        "info": (
            "The number of filters to use for the initial fully connected layer. The number of "
            "nodes actually used is: fc_min_filters x fc_dimensions x fc_dimensions.\nNB: This "
            "value may be scaled down, depending on output resolution."),
        "datatype": int,
        "rounding": 16,
        "min_max": (16, 5120),
        "group": "hidden layers",
        "fixed": True},
    "fc_max_filters": {
        "default": 1024,
        "info": (
            "This is the number of filters to be used in the final reshape layer at the end of "
            "the fully connected layers. The actual number of nodes used for the final fully "
            "connected layer is: fc_min_filters x fc_dimensions x fc_dimensions.\nNB: This value "
            "may be scaled down, depending on output resolution."),
        "datatype": int,
        "rounding": 64,
        "min_max": (128, 5120),
        "group": "hidden layers",
        "fixed": True},
    "fc_dimensions": {
        "default": 4,
        "info": (
            "The height and width dimension for the final reshape layer at the end of the "
            "fully connected layers.\nNB: The total number of nodes within the final fully "
            "connected layer will be: fc_dimensions x fc_dimensions x fc_max_filters."),
        "datatype": int,
        "rounding": 1,
        "min_max": (1, 16),
        "group": "hidden layers",
        "fixed": True},
    "fc_filter_slope": {
        "default": -0.5,
        "info": (
            "The rate that the filters move from the minimum number of filters to the maximum "
            "number of filters. EG:\n"
            "Negative numbers will change the number of filters quicker at first and slow down "
            "each layer.\n"
            "Positive numbers will change the number of filters slower at first but then speed "
            "up each layer.\n"
            "0.0 - This will change at a linear rate (i.e. the same number of filters will be "
            "changed at each layer)."),
        "datatype": float,
        "min_max": (-.99, .99),
        "rounding": 2,
        "group": "hidden layers",
        "fixed": True},
    "fc_dropout": {
        "default": 0.0,
        "info": (
            "Dropout is a form of regularization that can prevent a model from over-fitting "
            "and help to keep neurons 'alive'. 0.5 will dropout half the connections between each "
            "fully connected layer, 0.25 will dropout a quarter of the connections etc. Set to "
            "0.0 to disable."),
        "datatype": float,
        "rounding": 2,
        "min_max": (0.0, 0.99),
        "group": "hidden layers",
        "fixed": False},
    "fc_upsampler": {
        "default": "upsample2d",
        "info": (
            "The type of dimensional upsampling to perform at the end of the fully connected "
            "layers, if upsamples > 0. The number of filters used for the upscale layers will be "
            "the value given in 'fc_upsample_filters'."
            "\n\tupsample2d - A lightweight and VRAM friendly method. 'quick and dirty' but does "
            "not learn any parameters"
            "\n\tsubpixel - Sub-pixel upscaler using depth-to-space which may require more "
            "VRAM."
            "\n\tresize_images - Uses the Keras resize_image function to save about half as much "
            "vram as the heaviest methods."
            "\n\tupscale_fast - Developed by Andenixa. Focusses on speed to upscale, but "
            "requires more VRAM."
            "\n\tupscale_hybrid - Developed by Andenixa. Uses a combination of PixelShuffler and "
            "Upsampling2D to upscale, saving about 1/3rd of VRAM of the heaviest methods."),
        "datatype": str,
        "choices": ["resize_images", "subpixel", "upscale_fast", "upscale_hybrid", "upsample2d"],
        "group": "hidden layers",
        "gui_radio": False,
        "fixed": True},
    "fc_upsamples": {
        "default": 1,
        "info": (
            "Some upsampling can occur within the Fully Connected layers rather than in the "
            "Decoder to increase the dimensional space. Set how many upscale layers should occur "
            "within the Fully Connected layers."),
        "datatype": int,
        "min_max": (0, 4),
        "rounding": 1,
        "group": "hidden layers",
        "fixed": True},
    "fc_upsample_filters": {
        "default": 512,
        "info": (
            "If you have selected an upsampler which requires filters (i.e. any upsampler with "
            "the exception of Upsampling2D), then this is the number of filters to be used for "
            "the upsamplers within the fully connected layers,  NB: This value may be scaled "
            "down, depending on output resolution. Also note, that this figure will dictate the "
            "number of filters used for the G-Block, if selected."),
        "datatype": int,
        "rounding": 64,
        "min_max": (128, 5120),
        "group": "hidden layers",
        "fixed": True}, </s> remove         datatype:  [required] A python type class. This limits the type of data that can be
                   provided in the .ini file and ensures that the value is returned in the
                   correct type to faceswap. Valid data types are: <class 'int'>, <class 'float'>,
                   <class 'str'>, <class 'bool'>.
        default:   [required] The default value for this option.
        info:      [required] A string describing what this option does.
        choices:   [optional] If this option's datatype is of <class 'str'> then valid
                   selections can be defined here. This validates the option and also enables
                   a combobox / radio option in the GUI.
        gui_radio: [optional] If <choices> are defined, this indicates that the GUI should use
                   radio buttons rather than a combobox to display this option.
        min_max:   [partial] For <class 'int'> and <class 'float'> data types this is required
                   otherwise it is ignored. Should be a tuple of min and max accepted values.
                   This is used for controlling the GUI slider range. Values are not enforced.
        rounding:  [partial] For <class 'int'> and <class 'float'> data types this is
                   required otherwise it is ignored. Used for the GUI slider. For floats, this
                   is the number of decimal places to display. For ints this is the step size.
        fixed:     [optional] [train only]. Training configurations are fixed when the model is
                   created, and then reloaded from the state file. Marking an item as fixed=False
                   indicates that this value can be changed for existing models, and will override
                   the value saved in the state file with the updated value in config. If not
                   provided this will default to True.
 </s> add         "datatype:  [required] A python type class. This limits the type of data that can be
               "   provided in the .ini file and ensures that the value is returned in the
               "   correct type to faceswap. Valid data types are: <class 'int'>, <class 'float'>,
               "   <class 'str'>, <class 'bool'>.
        "default:   [required] The default value for this option.
        "info:      [required] A string describing what this option does.
        "choices:   [optional] If this option's datatype is of <class 'str'> then valid
               "   selections can be defined here. This validates the option and also enables
               "   a combobox / radio option in the GUI.
        "gui_radio: [optional] If <choices> are defined, this indicates that the GUI should use
               "   radio buttons rather than a combobox to display this option.
        "min_max:   [partial] For <class 'int'> and <class 'float'> data types this is required
               "   otherwise it is ignored. Should be a tuple of min and max accepted values.
               "   This is used for controlling the GUI slider range. Values are not enforced.
        "rounding:  [partial] For <class 'int'> and <class 'float'> data types this is
               "   required otherwise it is ignored. Used for the GUI slider. For floats, this
               "   is the number of decimal places to display. For ints this is the step size.
        "fixed:     [optional] [train only]. Training configurations are fixed when the model is
               "   created, and then reloaded from the state file. Marking an item as fixed=False
               "   indicates that this value can be changed for existing models, and will override
               "   the value saved in the state file with the updated value in config. If not
               "   provided this will default to True.
"NVIDIA GPUs can run operations in float16 faster than in "
  msgstr ""
  
  #: plugins/train/_config.py:299
  msgid ""
"NVIDIA GPUs can run operations in float16 faster than in "
  "float32. Mixed precision allows you to use a mix of float16 with float32, to "
  "get the performance benefits from float16 and the numeric stability benefits "
  "from float32.\n"
  "\n"
  "This is untested on DirectML backend, but will run on most Nvidia models. it "
keep keep keep keep replace keep keep keep keep keep
 Remove plaidML support (#1325)

* Remove PlaidML reference from readme files

* Remove AMD option from installers

* remove amd requirements and update setup.py

* remove plaidml test from CI workflow

* gpustats: remove plaidml backend

* plaid removals:
  - faceswap.py - python version check
  - setup.cfg - plaidml typing ignore
  - lib.keras_utils - All plaid code
  - lib.launcher.py - All plaidml checks and configuration

* remove tf2.2 specific code from GUI event reader

* lib.model - remove all plaidml implementations

* plugins.extract - remove plaidml code

* plugins.train remove plaidml code

* lib.convert - remove plaidml code

* tools.model: remove plaidml code

* Remove plaidML tests from unit tests

* remove plaidml_utils and docsting cleanups

* Remove plaidML refs from configs

* fix keras imports 
locales/plugins.train._config.pot