      return [m.group(0) for m in matches]
  
  
  def safe_shutdown(got_error=False):
      """ Close queues, threads and processes in event of crash """
      logger = logging.getLogger(__name__)  # pylint:disable=invalid-name
      logger.debug("Safely shutting down")
      from lib.queue_manager import queue_manager  # pylint:disable=import-outside-toplevel
      queue_manager.terminate_queues()
      logger.debug("Cleanup complete. Shutting down queue manager and exiting")
 
</s> remove         from: https://stackoverflow.com/questions/29916065 """
 </s> add     Parameters
    ----------
    identifier: str
        The camel case text to be split

    Returns
    -------
    list
        A list of the given identifier split into it's constituent parts


    References
    ----------
    https://stackoverflow.com/questions/29916065
    """ </s> remove def backup_file(directory, filename):
    """ Backup a given file by appending .bk to the end """
    logger = logging.getLogger(__name__)  # pylint:disable=invalid-name
    logger.trace("Backing up: '%s'", filename)
    origfile = os.path.join(directory, filename)
    backupfile = origfile + '.bk'
    if os.path.exists(backupfile):
        logger.trace("Removing existing file: '%s'", backup_file)
        os.remove(backupfile)
    if os.path.exists(origfile):
        logger.trace("Renaming: '%s' to '%s'", origfile, backup_file)
        os.rename(origfile, backupfile)


def set_system_verbosity(loglevel):
    """ Set the verbosity level of tensorflow and suppresses
        future and deprecation warnings from any modules
        From:
        https://stackoverflow.com/questions/35911252/disable-tensorflow-debugging-information
        Can be set to:
        0 - all logs shown
        1 - filter out INFO logs
        2 - filter out WARNING logs
        3 - filter out ERROR logs  """
 </s> add def set_system_verbosity(log_level):
    """ Set the verbosity level of tensorflow and suppresses future and deprecation warnings from
    any modules

    Parameters
    ----------
    log_level: str
        The requested Faceswap log level

    References
    ----------
    https://stackoverflow.com/questions/35911252/disable-tensorflow-debugging-information
    Can be set to:
    0: all logs shown. 1: filter out INFO logs. 2: filter out WARNING logs. 3: filter out ERROR
    logs.
    """ </s> remove     numeric_level = get_loglevel(loglevel)
    loglevel = "2" if numeric_level > 15 else "0"
    logger.debug("System Verbosity level: %s", loglevel)
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = loglevel
    if loglevel != '0':
 </s> add     numeric_level = get_loglevel(log_level)
    log_level = "2" if numeric_level > 15 else "0"
    logger.debug("System Verbosity level: %s", log_level)
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = log_level
    if log_level != '0': </s> remove             # Logger is held internally, as we don't want to log
            # when obtaining system stats on crash
 </s> add             # Logger held internally, as we don't want to log when obtaining system stats on crash </s> remove     freeze_log = list(debug_buffer)
 </s> add     freeze_log = list(_debug_buffer)
    """ Close all tracked queues and threads in event of crash or on shut down.

    Parameters
    ----------
    got_error: bool, optional
        ``True`` if this function is being called as the result of raised error, otherwise
        ``False``. Default: ``False``
    """
      return [m.group(0) for m in matches]
  
  
  def safe_shutdown(got_error=False):
    """ Close all tracked queues and threads in event of crash or on shut down.

    Parameters
    ----------
    got_error: bool, optional
        ``True`` if this function is being called as the result of raised error, otherwise
        ``False``. Default: ``False``
    """
      logger = logging.getLogger(__name__)  # pylint:disable=invalid-name
      logger.debug("Safely shutting down")
      from lib.queue_manager import queue_manager  # pylint:disable=import-outside-toplevel
      queue_manager.terminate_queues()
      logger.debug("Cleanup complete. Shutting down queue manager and exiting")
keep keep keep keep replace keep keep keep keep keep
 Faceswap 2.0 (#1045)

* Core Updates
    - Remove lib.utils.keras_backend_quiet and replace with get_backend() where relevant
    - Document lib.gpu_stats and lib.sys_info
    - Remove call to GPUStats.is_plaidml from convert and replace with get_backend()
    - lib.gui.menu - typofix

* Update Dependencies
Bump Tensorflow Version Check

* Port extraction to tf2

* Add custom import finder for loading Keras or tf.keras depending on backend

* Add `tensorflow` to KerasFinder search path

* Basic TF2 training running

* model.initializers - docstring fix

* Fix and pass tests for tf2

* Replace Keras backend tests with faceswap backend tests

* Initial optimizers update

* Monkey patch tf.keras optimizer

* Remove custom Adam Optimizers and Memory Saving Gradients

* Remove multi-gpu option. Add Distribution to cli

* plugins.train.model._base: Add Mirror, Central and Default distribution strategies

* Update tensorboard kwargs for tf2

* Penalized Loss - Fix for TF2 and AMD

* Fix syntax for tf2.1

* requirements typo fix

* Explicit None for clipnorm if using a distribution strategy

* Fix penalized loss for distribution strategies

* Update Dlight

* typo fix

* Pin to TF2.2

* setup.py - Install tensorflow from pip if not available in Conda

* Add reduction options and set default for mirrored distribution strategy

* Explicitly use default strategy rather than nullcontext

* lib.model.backup_restore documentation

* Remove mirrored strategy reduction method and default based on OS

* Initial restructure - training

* Remove PingPong
Start model.base refactor

* Model saving and resuming enabled

* More tidying up of model.base

* Enable backup and snapshotting

* Re-enable state file
Remove loss names from state file
Fix print loss function
Set snapshot iterations correctly

* Revert original model to Keras Model structure rather than custom layer
Output full model and sub model summary
Change NNBlocks to callables rather than custom keras layers

* Apply custom Conv2D layer

* Finalize NNBlock restructure
Update Dfaker blocks

* Fix reloading model under a different distribution strategy

* Pass command line arguments through to trainer

* Remove training_opts from model and reference params directly

* Tidy up model __init__

* Re-enable tensorboard logging
Suppress "Model Not Compiled" warning

* Fix timelapse

* lib.model.nnblocks - Bugfix residual block
Port dfaker
bugfix original

* dfl-h128 ported

* DFL SAE ported

* IAE Ported

* dlight ported

* port lightweight

* realface ported

* unbalanced ported

* villain ported

* lib.cli.args - Update Batchsize + move allow_growth to config

* Remove output shape definition
Get image sizes per side rather than globally

* Strip mask input from encoder

* Fix learn mask and output learned mask to preview

* Trigger Allow Growth prior to setting strategy

* Fix GUI Graphing

* GUI - Display batchsize correctly + fix training graphs

* Fix penalized loss

* Enable mixed precision training

* Update analysis displayed batch to match input

* Penalized Loss - Multi-GPU Fix

* Fix all losses for TF2

* Fix Reflect Padding

* Allow different input size for each side of the model

* Fix conv-aware initialization on reload

* Switch allow_growth order

* Move mixed_precision to cli

* Remove distrubution strategies

* Compile penalized loss sub-function into LossContainer

* Bump default save interval to 250
Generate preview on first iteration but don't save
Fix iterations to start at 1 instead of 0
Remove training deprecation warnings
Bump some scripts.train loglevels

* Add ability to refresh preview on demand on pop-up window

* Enable refresh of training preview from GUI

* Fix Convert
Debug logging in Initializers

* Fix Preview Tool

* Update Legacy TF1 weights to TF2
Catch stats error on loading stats with missing logs

* lib.gui.popup_configure - Make more responsive + document

* Multiple Outputs supported in trainer
Original Model - Mask output bugfix

* Make universal inference model for convert
Remove scaling from penalized mask loss (now handled at input to y_true)

* Fix inference model to work properly with all models

* Fix multi-scale output for convert

* Fix clipnorm issue with distribution strategies
Edit error message on OOM

* Update plaidml losses

* Add missing file

* Disable gmsd loss for plaidnl

* PlaidML - Basic training working

* clipnorm rewriting for mixed-precision

* Inference model creation bugfixes

* Remove debug code

* Bugfix: Default clipnorm to 1.0

* Remove all mask inputs from training code

* Remove mask inputs from convert

* GUI - Analysis Tab - Docstrings

* Fix rate in totals row

* lib.gui - Only update display pages if they have focus

* Save the model on first iteration

* plaidml - Fix SSIM loss with penalized loss

* tools.alignments - Remove manual and fix jobs

* GUI - Remove case formatting on help text

* gui MultiSelect custom widget - Set default values on init

* vgg_face2 - Move to plugins.extract.recognition and use plugins._base base class
cli - Add global GPU Exclude Option
tools.sort - Use global GPU Exlude option for backend
lib.model.session - Exclude all GPUs when running in CPU mode
lib.cli.launcher - Set backend to CPU mode when all GPUs excluded

* Cascade excluded devices to GPU Stats

* Explicit GPU selection for Train and Convert

* Reduce Tensorflow Min GPU Multiprocessor Count to 4

* remove compat.v1 code from extract

* Force TF to skip mixed precision compatibility check if GPUs have been filtered

* Add notes to config for non-working AMD losses

* Rasie error if forcing extract to CPU mode

* Fix loading of legace dfl-sae weights + dfl-sae typo fix

* Remove unused requirements
Update sphinx requirements
Fix broken rst file locations

* docs: lib.gui.display

* clipnorm amd condition check

* documentation - gui.display_analysis

* Documentation - gui.popup_configure

* Documentation - lib.logger

* Documentation - lib.model.initializers

* Documentation - lib.model.layers

* Documentation - lib.model.losses

* Documentation - lib.model.nn_blocks

* Documetation - lib.model.normalization

* Documentation - lib.model.session

* Documentation - lib.plaidml_stats

* Documentation: lib.training_data

* Documentation: lib.utils

* Documentation: plugins.train.model._base

* GUI Stats: prevent stats from using GPU

* Documentation - Original Model

* Documentation: plugins.model.trainer._base

* linting

* unit tests: initializers + losses

* unit tests: nn_blocks

* bugfix - Exclude gpu devices in train, not include

* Enable Exclude-Gpus in Extract

* Enable exclude gpus in tools

* Disallow multiple plugin types in a single model folder

* Automatically add exclude_gpus argument in for cpu backends

* Cpu backend fixes

* Relax optimizer test threshold

* Default Train settings - Set mask to Extended

* Update Extractor cli help text
Update to Python 3.8

* Fix FAN to run on CPU

* lib.plaidml_tools - typofix

* Linux installer - check for curl

* linux installer - typo fix 
lib/utils.py