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
  
  
  lowercase__			=	logging.get_logger(__name__)
  
  if is_tf_available():
         import tensorflow as tf
  
  
  
  
  
  
  @dataclass
  class 						lowercase     (   UpperCAmelCase_):
        __lowercase 		=  '''tf'''
        __lowercase 		=  field(
            default=UpperCAmelCase_				,			metadata={'''help''': '''Name of TPU'''}				,			)
  
        __lowercase 		=  field(
            default=UpperCAmelCase_				,			metadata={'''help''': '''Zone of TPU'''}				,			)
  
        __lowercase 		=  field(
            default=UpperCAmelCase_				,			metadata={'''help''': '''Name of Cloud TPU-enabled project'''}				,			)
  
        __lowercase 		=  field(
            default=1.0				,			metadata={'''help''': '''Power for the Polynomial decay LR scheduler.'''}				,			)
  
        __lowercase 		=  field(default=UpperCAmelCase_				,			metadata={'''help''': '''Whether to activate the XLA compilation or not'''})
  
  
  
  
  
  
        @cached_property
        def    A__(self					)       ->   Tuple["tf.distribute.Strategy", int]:
           requires_backends(self ,  ['''tf''']					)
           logger.info('''Tensorflow: setting up strategy'''					)
  
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= 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:
              SCREAMING_SNAKE_CASE__			:				Tuple       						= tf.distribute.OneDeviceStrategy(device='''/cpu:0'''					)
           else:
              try:
                 if self.tpu_name:
                    SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= tf.distribute.cluster_resolver.TPUClusterResolver(
                        self.tpu_name ,  zone=self.tpu_zone ,  project=self.gcp_project					)
                 else:
                    SCREAMING_SNAKE_CASE__			:				Dict       						= tf.distribute.cluster_resolver.TPUClusterResolver()
              except ValueError:
                 if self.tpu_name:
                    raise RuntimeError(f"""Couldn't connect to TPU {self.tpu_name}!"""					)
                 else:
                    SCREAMING_SNAKE_CASE__			:				Optional[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(a__					)
                 tf.tpu.experimental.initialize_tpu_system(a__					)
  
                 SCREAMING_SNAKE_CASE__			:				int       						= tf.distribute.TPUStrategy(a__					)
  
              elif len(a__					) == 0:
                 SCREAMING_SNAKE_CASE__			:				int       						= tf.distribute.OneDeviceStrategy(device='''/cpu:0'''					)
              elif len(a__					) == 1:
                 SCREAMING_SNAKE_CASE__			:				str       						= tf.distribute.OneDeviceStrategy(device='''/gpu:0'''					)
              elif len(a__					) > 1:
                 # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
                 SCREAMING_SNAKE_CASE__			:				Optional[int]       						= tf.distribute.MirroredStrategy()
              else:
                 raise ValueError('''Cannot find the proper strategy, please check your environment properties.'''					)
  
           return strategy
  
  
  
  
  
  
        @property
        def    A__(self					)       ->   "tf.distribute.Strategy":
           requires_backends(self ,  ['''tf''']					)
           return self._setup_strategy
  
  
  
  
  
  
        @property
        def    A__(self					)       ->   int:
           requires_backends(self ,  ['''tf''']					)
           return self._setup_strategy.num_replicas_in_sync
  
  
  
  
  
  
        @property
        def    A__(self					)       ->   Optional[int]:
           return False  # TF Logging is handled by Keras not the Trainer
  
  
  
  
  
  
        @property
        def    A__(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.'''					)
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= 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					)       ->   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.'''					)
           SCREAMING_SNAKE_CASE__			:				Dict       						= 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:
           requires_backends(self ,  ['''tf''']					)
           warnings.warn(
               '''The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.''' ,  a__ ,  )
           return self._setup_strategy.num_replicas_in_sync
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  lowercase__			=	{
      '''+''': lambda x, y: x + y,
      '''-''': lambda x, y: x - y,
      '''*''': lambda x, y: x * y,
      '''/''': lambda x, y: x / y,
  }
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     return c.isdigit()
  
  
  def 				UpperCamelCase	(  snake_case   ):
  
  
  
  
     '''simple docstring'''
  
     SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= []
  
     # iterate over the string in reverse order
     for c in expression.split()[::-1]:
        # push operand to stack
        if is_operand(snake_case   ):
           stack.append(int(snake_case   )   )
  
        else:
           # pop values from stack can calculate the result
           # push the result onto the stack again
           SCREAMING_SNAKE_CASE__			:				Dict       						= stack.pop()
           SCREAMING_SNAKE_CASE__			:				Any       						= stack.pop()
           stack.append(calc[c](snake_case	,     snake_case   )   )
  
     return stack.pop()
  
  
  # Driver code
  if __name__ == "__main__":
         lowercase__			=	'''+ 9 * 2 6'''
         print(evaluate(test_expression))
  
         lowercase__			=	'''/ * 10 2 + 4 1 '''
         print(evaluate(test_expression))
  
  