code: 
  
  
  
  
  
  
  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  							=  logging.get_logger(__name__)
  
  if is_tf_available():
     import tensorflow as tf
  
  
  
  @dataclass
  class 							_A (    UpperCamelCase							):
      """simple docstring"""
  
      lowerCamelCase  :       int   =   '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 _a       (							self			:    Optional[Any]			)			->       Tuple["tf.distribute.Strategy", int]:
             requires_backends(self   ,    ["""tf"""]			)
             logger.info("""Tensorflow: setting up strategy"""			)
  
             __UpperCAmelCase    							=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:
                    __UpperCAmelCase    							=tf.distribute.OneDeviceStrategy(device="""/cpu:0"""			)
             else:
                    try:
                           if self.tpu_name:
                                  __UpperCAmelCase    							=tf.distribute.cluster_resolver.TPUClusterResolver(
                                      self.tpu_name   ,    zone=self.tpu_zone   ,    project=self.gcp_project			)
                           else:
                                  __UpperCAmelCase    							=tf.distribute.cluster_resolver.TPUClusterResolver()
                    except ValueError:
                           if self.tpu_name:
                                  raise RuntimeError(f'''Couldn\'t connect to TPU {self.tpu_name}!'''			)
                           else:
                                  __UpperCAmelCase    							=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			)
  
                           __UpperCAmelCase    							=tf.distribute.TPUStrategy(__SCREAMING_SNAKE_CASE			)
  
                    elif len(__SCREAMING_SNAKE_CASE			) == 0:
                           __UpperCAmelCase    							=tf.distribute.OneDeviceStrategy(device="""/cpu:0"""			)
                    elif len(__SCREAMING_SNAKE_CASE			) == 1:
                           __UpperCAmelCase    							=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`
                           __UpperCAmelCase    							=tf.distribute.MirroredStrategy()
                    else:
                           raise ValueError("""Cannot find the proper strategy, please check your environment properties."""			)
  
             return strategy
  
  
  
      @property
      def _a       (							self			:    List[str]			)			->       "tf.distribute.Strategy":
             requires_backends(self   ,    ["""tf"""]			)
             return self._setup_strategy
  
  
  
      @property
      def _a       (							self			:    Any			)			->       int:
             requires_backends(self   ,    ["""tf"""]			)
             return self._setup_strategy.num_replicas_in_sync
  
  
  
      @property
      def _a       (							self			:    Union[str, Any]			)			->       Tuple:
             return False  # TF Logging is handled by Keras not the Trainer
  
  
  
      @property
      def _a       (							self			:    Dict			)			->       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."""			)
             __UpperCAmelCase    							=self.per_gpu_train_batch_size or self.per_device_train_batch_size
             return per_device_batch_size * self.n_replicas
  
  
  
      @property
      def _a       (							self			:    List[str]			)			->       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."""			)
             __UpperCAmelCase    							=self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
             return per_device_batch_size * self.n_replicas
  
  
  
      @property
      def _a       (							self			:    Tuple			)			->       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: 
  
  
  
  
  
  
  from collections import defaultdict
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	int = 5		)  ->							int:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=defaultdict(A_		)
         __UpperCAmelCase    							=0
  
         while True:
                __UpperCAmelCase    							=get_digits(A_		)
                freqs[digits].append(A_		)
  
                if len(freqs[digits]		) == max_base:
                       __UpperCAmelCase    							=freqs[digits][0] ** 3
                       return base
  
                num += 1
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	int		)  ->							str:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return "".join(sorted(str(num**3		)		)		)
  
  
  if __name__ == "__main__":
     print(F"""{solution() = }""")
  
  
  
  
  