


'''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










'''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()






