code: 
  
  
  '''simple docstring'''
  
  
  
  import warnings
  from dataclasses import dataclass, field
  from typing import Optional, Tuple
  
  from .training_args import TrainingArguments
  from .utils import cached_property, is_tf_available, logging, requires_backends
  
  
  A__	:      Optional[int]				=						logging.get_logger(__name__)
  
  if is_tf_available():
        import tensorflow as tf
  
  
  
  
  
  @dataclass
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Union[str, Any]							  =    'tf'
      lowerCamelCase						:      Optional[str]							  =    field(
          default=_UpperCAmelCase      ,	metadata={'help': 'Name of TPU'}      ,	)
  
      lowerCamelCase						:      Optional[str]							  =    field(
          default=_UpperCAmelCase      ,	metadata={'help': 'Zone of TPU'}      ,	)
  
      lowerCamelCase						:      Optional[str]							  =    field(
          default=_UpperCAmelCase      ,	metadata={'help': 'Name of Cloud TPU-enabled project'}      ,	)
  
      lowerCamelCase						:      float							  =    field(
          default=1.0      ,	metadata={'help': 'Power for the Polynomial decay LR scheduler.'}      ,	)
  
      lowerCamelCase						:      bool							  =    field(default=_UpperCAmelCase      ,	metadata={'help': 'Whether to activate the XLA compilation or not'}						)
  
  
  
  
  
  
  
      @cached_property
      def    lowercase_       (							self			)       ->  Tuple["tf.distribute.Strategy", int]:
           requires_backends(self    ,  ['tf']			)
           logger.info('Tensorflow: setting up strategy'			)
  
           __lowerCamelCase     :			Dict              =	tf.config.list_physical_devices('GPU'			)
  
           # Set to float16 at first
           if self.fpaa:
                tf.keras.mixed_precision.set_global_policy('mixed_float16'			)
  
           if self.no_cuda:
                __lowerCamelCase     :			str              =	tf.distribute.OneDeviceStrategy(device='/cpu:0'			)
           else:
                try:
                     if self.tpu_name:
                          __lowerCamelCase     :			Optional[int]              =	tf.distribute.cluster_resolver.TPUClusterResolver(
                              self.tpu_name    ,  zone=self.tpu_zone    ,  project=self.gcp_project			)
                     else:
                          __lowerCamelCase     :			Optional[Any]              =	tf.distribute.cluster_resolver.TPUClusterResolver()
                except ValueError:
                     if self.tpu_name:
                          raise RuntimeError(f'Couldn\'t connect to TPU {self.tpu_name}!'			)
                     else:
                          __lowerCamelCase     :			int              =	None
  
                if tpu:
                     # Set to bfloat16 in case of TPU
                     if self.fpaa:
                          tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'			)
  
                     tf.config.experimental_connect_to_cluster(SCREAMING_SNAKE_CASE_			)
                     tf.tpu.experimental.initialize_tpu_system(SCREAMING_SNAKE_CASE_			)
  
                     __lowerCamelCase     :			str              =	tf.distribute.TPUStrategy(SCREAMING_SNAKE_CASE_			)
  
                elif len(SCREAMING_SNAKE_CASE_			) == 0:
                     __lowerCamelCase     :			str              =	tf.distribute.OneDeviceStrategy(device='/cpu:0'			)
                elif len(SCREAMING_SNAKE_CASE_			) == 1:
                     __lowerCamelCase     :			str              =	tf.distribute.OneDeviceStrategy(device='/gpu:0'			)
                elif len(SCREAMING_SNAKE_CASE_			) > 1:
                     # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
                     __lowerCamelCase     :			Union[str, Any]              =	tf.distribute.MirroredStrategy()
                else:
                     raise ValueError('Cannot find the proper strategy, please check your environment properties.'			)
  
           return strategy
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  "tf.distribute.Strategy":
           requires_backends(self    ,  ['tf']			)
           return self._setup_strategy
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  int:
           requires_backends(self    ,  ['tf']			)
           return self._setup_strategy.num_replicas_in_sync
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  List[Any]:
           return False  # TF Logging is handled by Keras not the Trainer
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  int:
           if self.per_gpu_train_batch_size:
                logger.warning(
                    'Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future '
                    'version. Using `--per_device_train_batch_size` is preferred.'			)
           __lowerCamelCase     :			Union[str, Any]              =	self.per_gpu_train_batch_size or self.per_device_train_batch_size
           return per_device_batch_size * self.n_replicas
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  int:
           if self.per_gpu_eval_batch_size:
                logger.warning(
                    'Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future '
                    'version. Using `--per_device_eval_batch_size` is preferred.'			)
           __lowerCamelCase     :			int              =	self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
           return per_device_batch_size * self.n_replicas
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  int:
           requires_backends(self    ,  ['tf']			)
           warnings.warn(
               'The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.'    ,  SCREAMING_SNAKE_CASE_    ,  )
           return self._setup_strategy.num_replicas_in_sync
  
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       str							)  ->	int:
       __lowerCamelCase     :			Tuple              =	str(UpperCAmelCase_							).strip()
       if not bin_string:
            raise ValueError('Empty string was passed to the function'							)
       __lowerCamelCase     :			Optional[int]              =	bin_string[0] == '-'
       if is_negative:
            __lowerCamelCase     :			List[Any]              =	bin_string[1:]
       if not all(char in '01' for char in bin_string							):
            raise ValueError('Non-binary value was passed to the function'							)
       __lowerCamelCase     :			Optional[int]              =	0
       for char in bin_string:
            __lowerCamelCase     :			str              =	2 * decimal_number + int(UpperCAmelCase_							)
       return -decimal_number if is_negative else decimal_number
  
  
  if __name__ == "__main__":
        from doctest import testmod
  
        testmod()
  
  
  
  
  
  
  