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
  
  
  snake_case_    						=				logging.get_logger(__name__)
  
  if is_tf_available():
    import tensorflow as tf
  
  
  @dataclass
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
      __lowerCamelCase		: str														=  """tf"""
      __lowerCamelCase		: Optional[str]														=  field(
          default=__snake_case	, metadata={"""help""": """Name of TPU"""}	, )
  
      __lowerCamelCase		: Optional[str]														=  field(
          default=__snake_case	, metadata={"""help""": """Zone of TPU"""}	, )
  
      __lowerCamelCase		: Optional[str]														=  field(
          default=__snake_case	, 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=__snake_case	, metadata={"""help""": """Whether to activate the XLA compilation or not"""}  )
  
  
  
  
  
      @cached_property
      def    snake_case_       (   self):
           requires_backends(self		,     ['tf'])
           logger.info('Tensorflow: setting up strategy')
  
           lowercase__					:     Optional[int]   = 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__					:     Dict   = tf.distribute.OneDeviceStrategy(device='/cpu:0')
           else:
                try:
                     if self.tpu_name:
                          lowercase__					:     str   = tf.distribute.cluster_resolver.TPUClusterResolver(
                              self.tpu_name		,     zone=self.tpu_zone		,     project=self.gcp_project)
                     else:
                          lowercase__					:     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(a)
                     tf.tpu.experimental.initialize_tpu_system(a)
  
                     lowercase__					:     Any   = tf.distribute.TPUStrategy(a)
  
                elif len(a) == 0:
                     lowercase__					:     List[Any]   = tf.distribute.OneDeviceStrategy(device='/cpu:0')
                elif len(a) == 1:
                     lowercase__					:     Dict   = 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`
                     lowercase__					:     str   = tf.distribute.MirroredStrategy()
                else:
                     raise ValueError('Cannot find the proper strategy, please check your environment properties.')
  
           return strategy
  
  
  
  
  
      @property
      def    snake_case_       (   self):
           requires_backends(self		,     ['tf'])
           return self._setup_strategy
  
  
  
  
  
      @property
      def    snake_case_       (   self):
           requires_backends(self		,     ['tf'])
           return self._setup_strategy.num_replicas_in_sync
  
  
  
  
  
      @property
      def    snake_case_       (   self):
           return False  # TF Logging is handled by Keras not the Trainer
  
  
  
  
  
      @property
      def    snake_case_       (   self):
           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__					:     List[Any]   = self.per_gpu_train_batch_size or self.per_device_train_batch_size
           return per_device_batch_size * self.n_replicas
  
  
  
  
  
      @property
      def    snake_case_       (   self):
           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__					:     Optional[Any]   = self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
           return per_device_batch_size * self.n_replicas
  
  
  
  
  
      @property
      def    snake_case_       (   self):
           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: 
  
  from __future__ import annotations
  
  
  class    SCREAMING_SNAKE_CASE__      :
  
  
  
  
  
      def __init__(   self		,     a = 0):
           lowercase__					:     int   = value
           lowercase__					:     Node | None   = None
           lowercase__					:     Node | None   = None
  
  
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Node | None			,  SCREAMING_SNAKE_CASE_	:					Node | None    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       if treea is None:
            return treea
       if treea is None:
            return treea
  
       lowercase__					:     Optional[Any]   = treea.value + treea.value
       lowercase__					:     Union[str, Any]   = merge_two_binary_trees(treea.left			,  treea.left    )
       lowercase__					:     Optional[Any]   = merge_two_binary_trees(treea.right			,  treea.right    )
       return treea
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Node | None    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       if root:
            print(root.value    )
            print_preorder(root.left    )
            print_preorder(root.right    )
  
  
  if __name__ == "__main__":
         snake_case_    						=				Node(1)
         snake_case_    						=				Node(2)
         snake_case_    						=				Node(3)
         snake_case_    						=				Node(4)
  
         snake_case_    						=				Node(2)
         snake_case_    						=				Node(4)
         snake_case_    						=				Node(6)
         snake_case_    						=				Node(9)
         snake_case_    						=				Node(5)
  
         print('''Tree1 is: ''')
         print_preorder(treea)
         print('''Tree2 is: ''')
         print_preorder(treea)
         snake_case_    						=				merge_two_binary_trees(treea, treea)
         print('''Merged Tree is: ''')
         print_preorder(merged_tree)
  
  