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												=	logging.get_logger(__name__)
  
  if is_tf_available():
  	import tensorflow as tf
  
  
  
  
  
  @dataclass
  class     __UpperCamelCase				(     lowerCAmelCase_		):
  		SCREAMING_SNAKE_CASE              =					'tf'
  		SCREAMING_SNAKE_CASE              =					field(
  		    default=lowerCAmelCase_  ,	metadata={"help": "Name of TPU"}  ,	)
  
  		SCREAMING_SNAKE_CASE              =					field(
  		    default=lowerCAmelCase_  ,	metadata={"help": "Zone of TPU"}  ,	)
  
  		SCREAMING_SNAKE_CASE              =					field(
  		    default=lowerCAmelCase_  ,	metadata={"help": "Name of Cloud TPU-enabled project"}  ,	)
  
  		SCREAMING_SNAKE_CASE              =					field(
  		    default=1.0  ,	metadata={"help": "Power for the Polynomial decay LR scheduler."}  ,	)
  
  		SCREAMING_SNAKE_CASE              =					field(default=lowerCAmelCase_  ,	metadata={"help": "Whether to activate the XLA compilation or not"}		)
  
  
  		@cached_property
  		def       SCREAMING_SNAKE_CASE__     (self				:	Any):
  							requires_backends(self  ,  ["tf"])
  							logger.info("Tensorflow: setting up strategy")
  
  							A    =      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:
  												A    =      tf.distribute.OneDeviceStrategy(device="/cpu:0")
  							else:
  												try:
  																	if self.tpu_name:
  																						A    =      tf.distribute.cluster_resolver.TPUClusterResolver(
  																						    self.tpu_name  ,  zone=self.tpu_zone  ,  project=self.gcp_project)
  																	else:
  																						A    =      tf.distribute.cluster_resolver.TPUClusterResolver()
  												except ValueError:
  																	if self.tpu_name:
  																						raise RuntimeError(F"""Couldn\'t connect to TPU {self.tpu_name}!""")
  																	else:
  																						A    =      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(lowerCamelCase_)
  																	tf.tpu.experimental.initialize_tpu_system(lowerCamelCase_)
  
  																	A    =      tf.distribute.TPUStrategy(lowerCamelCase_)
  
  												elif len(lowerCamelCase_) == 0:
  																	A    =      tf.distribute.OneDeviceStrategy(device="/cpu:0")
  												elif len(lowerCamelCase_) == 1:
  																	A    =      tf.distribute.OneDeviceStrategy(device="/gpu:0")
  												elif len(lowerCamelCase_) > 1:
  																	# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
  																	A    =      tf.distribute.MirroredStrategy()
  												else:
  																	raise ValueError("Cannot find the proper strategy, please check your environment properties.")
  
  							return strategy
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
  							requires_backends(self  ,  ["tf"])
  							return self._setup_strategy
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
  							requires_backends(self  ,  ["tf"])
  							return self._setup_strategy.num_replicas_in_sync
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple):
  							return False  # TF Logging is handled by Keras not the Trainer
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]):
  
  							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.")
  							A    =      self.per_gpu_train_batch_size or self.per_device_train_batch_size
  							return per_device_batch_size * self.n_replicas
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	str):
  
  							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.")
  							A    =      self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
  							return per_device_batch_size * self.n_replicas
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple):
  							requires_backends(self  ,  ["tf"])
  							warnings.warn(
  							    "The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead."  ,  lowerCamelCase_  ,  )
  							return self._setup_strategy.num_replicas_in_sync
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  					if not isinstance(lowercase__    ,							lowercase__   ):
  										A    =      (
  										    "operation can not be conducted on a object of type "
  										    F"""{type(lowercase__   ).__name__}"""
  										)
  										raise TypeError(lowercase__   )
  					A    =      ""
  					for _ in range(0    ,							32   ):
  										bit_string += str(number % 2   )
  										A    =      number >> 1
  					return bit_string
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  					if number < 0:
  										raise ValueError("the value of input must be positive"   )
  					elif isinstance(lowercase__    ,							lowercase__   ):
  										raise TypeError("Input value must be a 'int' type"   )
  					elif isinstance(lowercase__    ,							lowercase__   ):
  										raise TypeError("'<' not supported between instances of 'str' and 'int'"   )
  					A    =      0
  					# iterator over [1 to 32],since we are dealing with 32 bit integer
  					for _ in range(1    ,							33   ):
  										# left shift the bits by unity
  										A    =      result << 1
  										# get the end bit
  										A    =      number % 2
  										# right shift the bits by unity
  										A    =      number >> 1
  										# add that bit to our ans
  										A    =      result | end_bit
  					return get_reverse_bit_string(lowercase__   )
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  
  
  
  