
def        __lowercase(							__snake_case				:		dict    ,__snake_case				:		int					)       ->   List[str]:
       if not root or root not in binary_tree_mirror_dictionary:
              return
       __snake_case       ,       __snake_case						       =   binary_tree_mirror_dictionary[root][:2]
       __snake_case						       =   [right_child, left_child]
       binary_tree_mirror_dict(__snake_case    ,__snake_case					)
       binary_tree_mirror_dict(__snake_case    ,__snake_case					)
def        __lowercase(							__snake_case				:		dict    ,__snake_case				:		int = 1					)       ->   dict:
       if not binary_tree:
              raise ValueError('binary tree cannot be empty'					)
       if root not in binary_tree:
              __snake_case						       =   f'''root {root} is not present in the binary_tree'''
              raise ValueError(__snake_case					)
       __snake_case						       =   dict(__snake_case					)
       binary_tree_mirror_dict(__snake_case    ,__snake_case					)
       return binary_tree_mirror_dictionary


if __name__ == "__main__":
 lowerCamelCase_				:							Union[str, Any]       =    {1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
 print(f"""Binary tree: {binary_tree}""")
 lowerCamelCase_				:							Optional[Any]       =    binary_tree_mirror(binary_tree, 5)
 print(f"""Binary tree mirror: {binary_tree_mirror_dictionary}""")


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


lowerCamelCase_				:							Optional[Any]       =    logging.get_logger(__name__)

if is_tf_available():
 import tensorflow as tf







@dataclass
class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""tf"""
     lowercase__				  =			field(
         default=lowerCamelCase							,					metadata={"""help""": """Name of TPU"""}							,					)

     lowercase__				  =			field(
         default=lowerCamelCase							,					metadata={"""help""": """Zone of TPU"""}							,					)

     lowercase__				  =			field(
         default=lowerCamelCase							,					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=lowerCamelCase							,					metadata={"""help""": """Whether to activate the XLA compilation or not"""}					)

     @cached_property
     def 						__lowerCamelCase				(  self    ):
            requires_backends(self							,       ['tf']    )
            logger.info('Tensorflow: setting up strategy'    )

            __snake_case						       =   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:
                   __snake_case						       =   tf.distribute.OneDeviceStrategy(device='/cpu:0'    )
            else:
                   try:
                          if self.tpu_name:
                                 __snake_case						       =   tf.distribute.cluster_resolver.TPUClusterResolver(
                                     self.tpu_name							,       zone=self.tpu_zone							,       project=self.gcp_project    )
                          else:
                                 __snake_case						       =   tf.distribute.cluster_resolver.TPUClusterResolver()
                   except ValueError:
                          if self.tpu_name:
                                 raise RuntimeError(f'''Couldn\'t connect to TPU {self.tpu_name}!'''    )
                          else:
                                 __snake_case						       =   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_    )

                          __snake_case						       =   tf.distribute.TPUStrategy(SCREAMING_SNAKE_CASE_    )

                   elif len(SCREAMING_SNAKE_CASE_    ) == 0:
                          __snake_case						       =   tf.distribute.OneDeviceStrategy(device='/cpu:0'    )
                   elif len(SCREAMING_SNAKE_CASE_    ) == 1:
                          __snake_case						       =   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`
                          __snake_case						       =   tf.distribute.MirroredStrategy()
                   else:
                          raise ValueError('Cannot find the proper strategy, please check your environment properties.'    )

            return strategy

     @property
     def 						__lowerCamelCase				(  self    ):
            requires_backends(self							,       ['tf']    )
            return self._setup_strategy

     @property
     def 						__lowerCamelCase				(  self    ):
            requires_backends(self							,       ['tf']    )
            return self._setup_strategy.num_replicas_in_sync

     @property
     def 						__lowerCamelCase				(  self    ):
            return False  # TF Logging is handled by Keras not the Trainer

     @property
     def 						__lowerCamelCase				(  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.'    )
            __snake_case						       =   self.per_gpu_train_batch_size or self.per_device_train_batch_size
            return per_device_batch_size * self.n_replicas

     @property
     def 						__lowerCamelCase				(  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.'    )
            __snake_case						       =   self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
            return per_device_batch_size * self.n_replicas

     @property
     def 						__lowerCamelCase				(  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.'							,       SCREAMING_SNAKE_CASE_							,       )
            return self._setup_strategy.num_replicas_in_sync
