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
  
  
  UpperCamelCase_	:    Union[str, Any]  					=       logging.get_logger(__name__)
  
  if is_tf_available():
  							import tensorflow as tf
  
  
  
  
  @dataclass
  class      __lowercase	(	__snake_case					):
  	_A   			=      "tf"
  	_A   			=      field(
  	    default=__snake_case      ,   metadata={"help": "Name of TPU"}      ,   )
  
  	_A   			=      field(
  	    default=__snake_case      ,   metadata={"help": "Zone of TPU"}      ,   )
  
  	_A   			=      field(
  	    default=__snake_case      ,   metadata={"help": "Name of Cloud TPU-enabled project"}      ,   )
  
  	_A   			=      field(
  	    default=1.0      ,   metadata={"help": "Power for the Polynomial decay LR scheduler."}      ,   )
  
  	_A   			=      field(default=__snake_case      ,   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"						)
  
  							_lowercase					:  str    			=      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:
  													_lowercase					:  Optional[int]    			=      tf.distribute.OneDeviceStrategy(device="/cpu:0"						)
  							else:
  													try:
  																			if self.tpu_name:
  																									_lowercase					:  List[Any]    			=      tf.distribute.cluster_resolver.TPUClusterResolver(
  																									    self.tpu_name			,  zone=self.tpu_zone			,  project=self.gcp_project						)
  																			else:
  																									_lowercase					:  Union[str, Any]    			=      tf.distribute.cluster_resolver.TPUClusterResolver()
  													except ValueError:
  																			if self.tpu_name:
  																									raise RuntimeError(F"""Couldn't connect to TPU {self.tpu_name}!"""						)
  																			else:
  																									_lowercase					:  Any    			=      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(snake_case						)
  																			tf.tpu.experimental.initialize_tpu_system(snake_case						)
  
  																			_lowercase					:  List[Any]    			=      tf.distribute.TPUStrategy(snake_case						)
  
  													elif len(snake_case						) == 0:
  																			_lowercase					:  Any    			=      tf.distribute.OneDeviceStrategy(device="/cpu:0"						)
  													elif len(snake_case						) == 1:
  																			_lowercase					:  str    			=      tf.distribute.OneDeviceStrategy(device="/gpu:0"						)
  													elif len(snake_case						) > 1:
  																			# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
  																			_lowercase					:  Union[str, Any]    			=      tf.distribute.MirroredStrategy()
  													else:
  																			raise ValueError("Cannot find the proper strategy, please check your environment properties."						)
  
  							return strategy
  
  
  
  
  
  
  	@property
  	def 			_a(self							:   Optional[int]						)   -> "tf.distribute.Strategy":
  							requires_backends(self			,  ["tf"]						)
  							return self._setup_strategy
  
  
  
  
  
  
  	@property
  	def 			_a(self							:   Optional[Any]						)   -> int:
  							requires_backends(self			,  ["tf"]						)
  							return self._setup_strategy.num_replicas_in_sync
  
  
  
  
  
  
  	@property
  	def 			_a(self							:   int						)   -> List[str]:
  							return False  # TF Logging is handled by Keras not the Trainer
  
  
  
  
  
  
  	@property
  	def 			_a(self							:   List[str]						)   -> 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."						)
  							_lowercase					:  int    			=      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							:   Dict						)   -> 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."						)
  							_lowercase					:  int    			=      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							:   int						)   -> 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."			,  snake_case			,  )
  							return self._setup_strategy.num_replicas_in_sync
  
  
  
  
  
style_context: 
  import math
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> list:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						if n <= 0:
  												raise ValueError("Only positive integers have prime factors"     )
  						_lowercase					:  Optional[int]    			=      []
  						while n % 2 == 0:
  												pf.append(2     )
  												_lowercase					:  Tuple    			=      int(n / 2     )
  						for i in range(3   , int(math.sqrt(_UpperCAmelCase     )     ) + 1   , 2     ):
  												while n % i == 0:
  																		pf.append(_UpperCAmelCase     )
  																		_lowercase					:  Optional[int]    			=      int(n / i     )
  						if n > 2:
  												pf.append(_UpperCAmelCase     )
  						return pf
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						if n <= 0:
  												raise ValueError("Only positive numbers are accepted"     )
  						_lowercase					:  str    			=      1
  						_lowercase					:  Tuple    			=      1
  						while n % 2 == 0:
  												temp += 1
  												_lowercase					:  List[Any]    			=      int(n / 2     )
  						div *= temp
  						for i in range(3   , int(math.sqrt(_UpperCAmelCase     )     ) + 1   , 2     ):
  												_lowercase					:  Optional[Any]    			=      1
  												while n % i == 0:
  																		temp += 1
  																		_lowercase					:  Optional[Any]    			=      int(n / i     )
  												div *= temp
  						if n > 1:
  												div *= 2
  						return div
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						if n <= 0:
  												raise ValueError("Only positive numbers are accepted"     )
  						_lowercase					:  int    			=      1
  						_lowercase					:  List[str]    			=      1
  						while n % 2 == 0:
  												temp += 1
  												_lowercase					:  str    			=      int(n / 2     )
  						if temp > 1:
  												s *= (2**temp - 1) / (2 - 1)
  						for i in range(3   , int(math.sqrt(_UpperCAmelCase     )     ) + 1   , 2     ):
  												_lowercase					:  int    			=      1
  												while n % i == 0:
  																		temp += 1
  																		_lowercase					:  List[Any]    			=      int(n / i     )
  												if temp > 1:
  																		s *= (i**temp - 1) / (i - 1)
  						return int(_UpperCAmelCase     )
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      int     )      -> int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  int    			=      n
  						for x in set(prime_factors(_UpperCAmelCase     )     ):
  												s *= (x - 1) / x
  						return int(_UpperCAmelCase     )
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  
  
  
  
  