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
  
  
  SCREAMING_SNAKE_CASE_     				=   logging.get_logger(__name__)
  
  if is_tf_available():
    import tensorflow as tf
  
  
  
  
  
  
  @dataclass
  class     __a			(					lowerCAmelCase_			):
         _lowerCamelCase							:							List[Any]       =			'tf'
         _lowerCamelCase							:							Optional[str]       =			field(
             default=lowerCAmelCase_				,	metadata={'help': 'Name of TPU'}				,	)
  
         _lowerCamelCase							:							Optional[str]       =			field(
             default=lowerCAmelCase_				,	metadata={'help': 'Zone of TPU'}				,	)
  
         _lowerCamelCase							:							Optional[str]       =			field(
             default=lowerCAmelCase_				,	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=lowerCAmelCase_				,	metadata={'help': 'Whether to activate the XLA compilation or not'}			)
  
  
  
  
  
  
  
         @cached_property
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            requires_backends(self							,["tf"]		)
            logger.info("Tensorflow: setting up strategy"		)
  
            _UpperCAmelCase   	=					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:
               _UpperCAmelCase   	=					tf.distribute.OneDeviceStrategy(device="/cpu:0"		)
            else:
               try:
                  if self.tpu_name:
                     _UpperCAmelCase   	=					tf.distribute.cluster_resolver.TPUClusterResolver(
                         self.tpu_name							,zone=self.tpu_zone							,project=self.gcp_project		)
                  else:
                     _UpperCAmelCase   	=					tf.distribute.cluster_resolver.TPUClusterResolver()
               except ValueError:
                  if self.tpu_name:
                     raise RuntimeError(f'''Couldn\'t connect to TPU {self.tpu_name}!'''		)
                  else:
                     _UpperCAmelCase   	=					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_		)
  
                  _UpperCAmelCase   	=					tf.distribute.TPUStrategy(lowerCamelCase_		)
  
               elif len(lowerCamelCase_		) == 0:
                  _UpperCAmelCase   	=					tf.distribute.OneDeviceStrategy(device="/cpu:0"		)
               elif len(lowerCamelCase_		) == 1:
                  _UpperCAmelCase   	=					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`
                  _UpperCAmelCase   	=					tf.distribute.MirroredStrategy()
               else:
                  raise ValueError("Cannot find the proper strategy, please check your environment properties."		)
  
            return strategy
  
  
  
  
  
  
  
         @property
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            requires_backends(self							,["tf"]		)
            return self._setup_strategy
  
  
  
  
  
  
  
         @property
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            requires_backends(self							,["tf"]		)
            return self._setup_strategy.num_replicas_in_sync
  
  
  
  
  
  
  
         @property
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            return False  # TF Logging is handled by Keras not the Trainer
  
  
  
  
  
  
  
         @property
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            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."		)
            _UpperCAmelCase   	=					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		):
  
  
  
            '''simple docstring'''
  
            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."		)
            _UpperCAmelCase   	=					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		):
  
  
  
            '''simple docstring'''
  
            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"""
  
  
  
  
  
  
  
  import numpy as np
  
  
  
  
  
  
  class     __a			:
  
  
  
  
  
  
  
         def __init__(					self							,lowerCamelCase_=5							,lowerCamelCase_=5		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					depth
            _UpperCAmelCase   	=					0
            _UpperCAmelCase   	=					None
            _UpperCAmelCase   	=					None
            _UpperCAmelCase   	=					min_leaf_size
            _UpperCAmelCase   	=					None
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            if labels.ndim != 1:
               print("Error: Input labels must be one dimensional"		)
  
            return np.mean((labels - prediction) ** 2		)
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            if x.ndim != 1:
               print("Error: Input data set must be one dimensional"		)
               return
            if len(lowerCamelCase_		) != len(lowerCamelCase_		):
               print("Error: X and y have different lengths"		)
               return
            if y.ndim != 1:
               print("Error: Data set labels must be one dimensional"		)
               return
  
            if len(lowerCamelCase_		) < 2 * self.min_leaf_size:
               _UpperCAmelCase   	=					np.mean(lowerCamelCase_		)
               return
  
            if self.depth == 1:
               _UpperCAmelCase   	=					np.mean(lowerCamelCase_		)
               return
  
            _UpperCAmelCase   	=					0
            _UpperCAmelCase   	=					self.mean_squared_error(lowerCamelCase_							,np.mean(lowerCamelCase_		)		) * 2
            for i in range(len(lowerCamelCase_		)		):
               if len(x[:i]		) < self.min_leaf_size:
                  continue
               elif len(x[i:]		) < self.min_leaf_size:
                  continue
               else:
                  _UpperCAmelCase   	=					self.mean_squared_error(x[:i]							,np.mean(y[:i]		)		)
                  _UpperCAmelCase   	=					self.mean_squared_error(x[i:]							,np.mean(y[i:]		)		)
                  _UpperCAmelCase   	=					error_left + error_right
                  if error < min_error:
                     _UpperCAmelCase   	=					i
                     _UpperCAmelCase   	=					error
  
            if best_split != 0:
               _UpperCAmelCase   	=					x[:best_split]
               _UpperCAmelCase   	=					y[:best_split]
               _UpperCAmelCase   	=					x[best_split:]
               _UpperCAmelCase   	=					y[best_split:]
  
               _UpperCAmelCase   	=					x[best_split]
               _UpperCAmelCase   	=					DecisionTree(
                   depth=self.depth - 1							,min_leaf_size=self.min_leaf_size		)
               _UpperCAmelCase   	=					DecisionTree(
                   depth=self.depth - 1							,min_leaf_size=self.min_leaf_size		)
               self.left.train(lowerCamelCase_							,lowerCamelCase_		)
               self.right.train(lowerCamelCase_							,lowerCamelCase_		)
            else:
               _UpperCAmelCase   	=					np.mean(lowerCamelCase_		)
  
            return
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            if self.prediction is not None:
               return self.prediction
            elif self.left or self.right is not None:
               if x >= self.decision_boundary:
                  return self.right.predict(lowerCamelCase_		)
               else:
                  return self.left.predict(lowerCamelCase_		)
            else:
               print("Error: Decision tree not yet trained"		)
               return None
  
  
  
  
  
  
  class     __a			:
  
  
  
  
  
  
  
         @staticmethod
         def    _snake_case			(					lowerCamelCase_							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					float(0		)
            for label in labels:
               squared_error_sum += (label - prediction) ** 2
  
            return float(squared_error_sum / labels.size		)
  
  
  
  def 			__UpperCAmelCase		(		)     ->       Dict:
     _UpperCAmelCase   	=					np.arange(-1.0    ,  1.0    ,  0.005				)
     _UpperCAmelCase   	=					np.sin(a_				)
  
     _UpperCAmelCase   	=					DecisionTree(depth=10    ,  min_leaf_size=10				)
     tree.train(a_    ,  a_				)
  
     _UpperCAmelCase   	=					(np.random.rand(10				) * 2) - 1
     _UpperCAmelCase   	=					np.array([tree.predict(a_				) for x in test_cases]				)
     _UpperCAmelCase   	=					np.mean((predictions - test_cases) ** 2				)
  
     print("Test values: " + str(a_				)				)
     print("Predictions: " + str(a_				)				)
     print("Average error: " + str(a_				)				)
  
  
  if __name__ == "__main__":
    main()
    import doctest
  
    doctest.testmod(name='mean_squarred_error', verbose=True)
  
  