

'''simple docstring'''
import numpy as np


class      a__						:


   def __init__(				self    ,       _UpperCamelCase=5    ,       _UpperCamelCase=5						):





      """simple docstring"""





      _lowercase    :   str      =     depth
      _lowercase    :   List[Any]      =     0
      _lowercase    :   List[Any]      =     None
      _lowercase    :   Optional[int]      =     None
      _lowercase    :   Dict      =     min_leaf_size
      _lowercase    :   Optional[Any]      =     None


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase						):





      """simple docstring"""





      if labels.ndim != 1:
         print("Error: Input labels must be one dimensional"						)

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


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase						):





      """simple docstring"""





      if x.ndim != 1:
         print("Error: Input data set must be one dimensional"						)
         return
      if len(_UpperCamelCase						) != len(_UpperCamelCase						):
         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(_UpperCamelCase						) < 2 * self.min_leaf_size:
         _lowercase    :   str      =     np.mean(_UpperCamelCase						)
         return

      if self.depth == 1:
         _lowercase    :   Optional[int]      =     np.mean(_UpperCamelCase						)
         return

      _lowercase    :   Tuple      =     0
      _lowercase    :   int      =     self.mean_squared_error(_UpperCamelCase    ,       np.mean(_UpperCamelCase						)						) * 2
      for i in range(len(_UpperCamelCase						)						):
         if len(x[:i]						) < self.min_leaf_size:
            continue
         elif len(x[i:]						) < self.min_leaf_size:
            continue
         else:
            _lowercase    :   Dict      =     self.mean_squared_error(x[:i]    ,       np.mean(y[:i]						)						)
            _lowercase    :   int      =     self.mean_squared_error(x[i:]    ,       np.mean(y[i:]						)						)
            _lowercase    :   Union[str, Any]      =     error_left + error_right
            if error < min_error:
               _lowercase    :   Optional[Any]      =     i
               _lowercase    :   str      =     error

      if best_split != 0:
         _lowercase    :   List[str]      =     x[:best_split]
         _lowercase    :   Optional[Any]      =     y[:best_split]
         _lowercase    :   Tuple      =     x[best_split:]
         _lowercase    :   List[Any]      =     y[best_split:]

         _lowercase    :   List[Any]      =     x[best_split]
         _lowercase    :   Tuple      =     DecisionTree(
             depth=self.depth - 1    ,       min_leaf_size=self.min_leaf_size						)
         _lowercase    :   List[Any]      =     DecisionTree(
             depth=self.depth - 1    ,       min_leaf_size=self.min_leaf_size						)
         self.left.train(_UpperCamelCase    ,       _UpperCamelCase						)
         self.right.train(_UpperCamelCase    ,       _UpperCamelCase						)
      else:
         _lowercase    :   Dict      =     np.mean(_UpperCamelCase						)

      return






   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase						):





      """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(_UpperCamelCase						)
         else:
            return self.left.predict(_UpperCamelCase						)
      else:
         print("Error: Decision tree not yet trained"						)
         return None




class      a__						:


   @staticmethod
   def 		_lowerCamelCase     (				_UpperCamelCase    ,       _UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   int      =     float(0						)
      for label in labels:
         squared_error_sum += (label - prediction) ** 2

      return float(squared_error_sum / labels.size						)







def       _A	(			)    ->			Any:
   _lowercase    :   List[Any]      =     np.arange(-1.0	,							1.0	,							0.005  )
   _lowercase    :   List[Any]      =     np.sin(snake_case  )

   _lowercase    :   str      =     DecisionTree(depth=10	,							min_leaf_size=10  )
   tree.train(snake_case	,							snake_case  )

   _lowercase    :   Union[str, Any]      =     (np.random.rand(10  ) * 2) - 1
   _lowercase    :   Dict      =     np.array([tree.predict(snake_case  ) for x in test_cases]  )
   _lowercase    :   str      =     np.mean((predictions - test_cases) ** 2  )

   print("Test values: " + str(snake_case  )  )
   print("Predictions: " + str(snake_case  )  )
   print("Average error: " + str(snake_case  )  )


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

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









'''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


_snake_case     						=						logging.get_logger(__name__)

if is_tf_available():
    import tensorflow as tf


@dataclass
class      a__						(							lowerCamelCase_						):
   _SCREAMING_SNAKE_CASE					:  int			 =							'tf'
   _SCREAMING_SNAKE_CASE					:  Optional[str]			 =							field(
       default=lowerCamelCase_						,   metadata={'help': 'Name of TPU'}						,   )

   _SCREAMING_SNAKE_CASE					:  Optional[str]			 =							field(
       default=lowerCamelCase_						,   metadata={'help': 'Zone of TPU'}						,   )

   _SCREAMING_SNAKE_CASE					:  Optional[str]			 =							field(
       default=lowerCamelCase_						,   metadata={'help': 'Name of Cloud TPU-enabled project'}						,   )

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

   _SCREAMING_SNAKE_CASE					:  bool			 =							field(default=lowerCamelCase_						,   metadata={'help': 'Whether to activate the XLA compilation or not'}						)


   @cached_property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      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    :   Optional[int]      =     tf.distribute.OneDeviceStrategy(device="/cpu:0"						)
      else:
         try:
            if self.tpu_name:
               _lowercase    :   int      =     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    :   Tuple      =     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(_UpperCamelCase						)
            tf.tpu.experimental.initialize_tpu_system(_UpperCamelCase						)

            _lowercase    :   List[str]      =     tf.distribute.TPUStrategy(_UpperCamelCase						)

         elif len(_UpperCamelCase						) == 0:
            _lowercase    :   Optional[Any]      =     tf.distribute.OneDeviceStrategy(device="/cpu:0"						)
         elif len(_UpperCamelCase						) == 1:
            _lowercase    :   Optional[int]      =     tf.distribute.OneDeviceStrategy(device="/gpu:0"						)
         elif len(_UpperCamelCase						) > 1:
            # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
            _lowercase    :   Any      =     tf.distribute.MirroredStrategy()
         else:
            raise ValueError("Cannot find the proper strategy, please check your environment properties."						)

      return strategy


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





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


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





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


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





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


   @property
   def 		_lowerCamelCase     (				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."						)
      _lowercase    :   str      =     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						):





      """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."						)
      _lowercase    :   List[Any]      =     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						):





      """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."    ,       _UpperCamelCase    ,       )
      return self._setup_strategy.num_replicas_in_sync






