





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     :			Tuple     						=	logging.get_logger(__name__)

if is_tf_available():
       import tensorflow as tf

@dataclass
class 			__A(    __lowercase			):
     __A							=							"tf"
     __A							=							field(
         default=__lowercase    ,      metadata={"help": "Name of TPU"}    ,      )

     __A							=							field(
         default=__lowercase    ,      metadata={"help": "Zone of TPU"}    ,      )

     __A							=							field(
         default=__lowercase    ,      metadata={"help": "Name of Cloud TPU-enabled project"}    ,      )

     __A							=							field(
         default=1.0    ,      metadata={"help": "Power for the Polynomial decay LR scheduler."}    ,      )

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

     @cached_property
     def 						_UpperCamelCase  (   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(A__   )
                          tf.tpu.experimental.initialize_tpu_system(A__   )

                          _UpperCamelCase														=    tf.distribute.TPUStrategy(A__   )

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

            return strategy

     @property
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            requires_backends(self,							['''tf''']   )
            return self._setup_strategy

     @property
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            requires_backends(self,							['''tf''']   )
            return self._setup_strategy.num_replicas_in_sync

     @property
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            return False  # TF Logging is handled by Keras not the Trainer

     @property
     def 						_UpperCamelCase  (   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 						_UpperCamelCase  (   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 						_UpperCamelCase  (   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.''',							A__,							)
            return self._setup_strategy.num_replicas_in_sync









import numpy as np



class     lowercase_       :



      def __init__(     self :							Any     ,   A__ :							Optional[int]=5     ,   A__ :							Any=5						)       ->	Any:
        _snake_case		=				depth
        _snake_case		=				0
        _snake_case		=				None
        _snake_case		=				None
        _snake_case		=				min_leaf_size
        _snake_case		=				None



      def 		UpperCamelCase_						(     self :							List[Any]     ,   A__ :							Tuple     ,   A__ :							Union[str, Any]						)       ->	int:
        if labels.ndim != 1:
          print('''Error: Input labels must be one dimensional'''						)

        return np.mean((labels - prediction) ** 2						)



      def 		UpperCamelCase_						(     self :							str     ,   A__ :							str     ,   A__ :							int						)       ->	Optional[Any]:
        if x.ndim != 1:
          print('''Error: Input data set must be one dimensional'''						)
          return
        if len(A__						) != len(A__						):
          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(A__						) < 2 * self.min_leaf_size:
          _snake_case		=				np.mean(A__						)
          return

        if self.depth == 1:
          _snake_case		=				np.mean(A__						)
          return

        _snake_case		=				0
        _snake_case		=				self.mean_squared_error(A__     ,   np.mean(A__						)						) * 2
        for i in range(len(A__						)						):
          if len(x[:i]						) < self.min_leaf_size:
            continue
          elif len(x[i:]						) < self.min_leaf_size:
            continue
          else:
            _snake_case		=				self.mean_squared_error(x[:i]     ,   np.mean(y[:i]						)						)
            _snake_case		=				self.mean_squared_error(x[i:]     ,   np.mean(y[i:]						)						)
            _snake_case		=				error_left + error_right
            if error < min_error:
              _snake_case		=				i
              _snake_case		=				error

        if best_split != 0:
          _snake_case		=				x[:best_split]
          _snake_case		=				y[:best_split]
          _snake_case		=				x[best_split:]
          _snake_case		=				y[best_split:]

          _snake_case		=				x[best_split]
          _snake_case		=				DecisionTree(
              depth=self.depth - 1     ,   min_leaf_size=self.min_leaf_size						)
          _snake_case		=				DecisionTree(
              depth=self.depth - 1     ,   min_leaf_size=self.min_leaf_size						)
          self.left.train(A__     ,   A__						)
          self.right.train(A__     ,   A__						)
        else:
          _snake_case		=				np.mean(A__						)

        return







      def 		UpperCamelCase_						(     self :							Optional[int]     ,   A__ :							Tuple						)       ->	Any:
        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(A__						)
          else:
            return self.left.predict(A__						)
        else:
          print('''Error: Decision tree not yet trained'''						)
          return None



class     lowercase_       :



      @staticmethod
      def 		UpperCamelCase_						(     A__ :							Union[str, Any]     ,   A__ :							Union[str, Any]						)       ->	int:
        _snake_case		=				float(0						)
        for label in labels:
          squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size						)



def  snake_case_()				->      Any:



  """simple docstring"""






  _snake_case		=				np.arange(-1.0					,			1.0					,			0.005	)
  _snake_case		=				np.sin(_UpperCamelCase	)

  _snake_case		=				DecisionTree(depth=10					,			min_leaf_size=10	)
  tree.train(_UpperCamelCase					,			_UpperCamelCase	)

  _snake_case		=				(np.random.rand(10	) * 2) - 1
  _snake_case		=				np.array([tree.predict(_UpperCamelCase	) for x in test_cases]	)
  _snake_case		=				np.mean((predictions - test_cases) ** 2	)

  print('''Test values: ''' + str(_UpperCamelCase	)	)
  print('''Predictions: ''' + str(_UpperCamelCase	)	)
  print('''Average error: ''' + str(_UpperCamelCase	)	)


if __name__ == "__main__":
 main()
 import doctest

 doctest.testmod(name='''mean_squarred_error''', verbose=True)

