"""simple docstring"""
import numpy as np




class   __snake_case			:



      def __init__(		self       ,     lowercase=5       ,     lowercase=5)      ->     Optional[Any]:





        '''simple docstring'''





        a__:	Dict     				=      depth
        a__:	List[str]     				=      0
        a__:	Tuple     				=      None
        a__:	Dict     				=      None
        a__:	Optional[Any]     				=      min_leaf_size
        a__:	Tuple     				=      None



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase)      ->     Tuple:





        '''simple docstring'''





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

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



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase)      ->     Optional[int]:





        '''simple docstring'''





        if x.ndim != 1:
          print('Error: Input data set must be one dimensional')
          return
        if len(lowercase) != len(lowercase):
          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(lowercase) < 2 * self.min_leaf_size:
          a__:	int     				=      np.mean(lowercase)
          return

        if self.depth == 1:
          a__:	List[str]     				=      np.mean(lowercase)
          return

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

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

          a__:	Tuple     				=      x[best_split]
          a__:	Union[str, Any]     				=      DecisionTree(
              depth=self.depth - 1       ,     min_leaf_size=self.min_leaf_size)
          a__:	Optional[int]     				=      DecisionTree(
              depth=self.depth - 1       ,     min_leaf_size=self.min_leaf_size)
          self.left.train(lowercase       ,     lowercase)
          self.right.train(lowercase       ,     lowercase)
        else:
          a__:	Dict     				=      np.mean(lowercase)

        return



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Optional[Any]:





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







class   __snake_case			:



      @staticmethod
      def     lowerCamelCase_  (		lowercase       ,     lowercase)      ->     Dict:





        '''simple docstring'''





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

        return float(squared_error_sum / labels.size)







def 		__a  (      )      ->Optional[Any]:
  a__:	Dict     				=      np.arange(-1.0						,	1.0						,	0.005  )
  a__:	Optional[Any]     				=      np.sin(_SCREAMING_SNAKE_CASE  )

  a__:	Tuple     				=      DecisionTree(depth=10						,	min_leaf_size=10  )
  tree.train(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

  a__:	int     				=      (np.random.rand(10  ) * 2) - 1
  a__:	List[Any]     				=      np.array([tree.predict(_SCREAMING_SNAKE_CASE  ) for x in test_cases]  )
  a__:	Tuple     				=      np.mean((predictions - test_cases) ** 2  )

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


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

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






"""simple docstring"""






from transformers.models.cpm.tokenization_cpm import CpmTokenizer
from transformers.testing_utils import custom_tokenizers

from ..xlnet.test_modeling_xlnet import XLNetModelTest


@custom_tokenizers
class 	A_       (     _UpperCAmelCase   ):





  """simple docstring"""

  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					int:
     return True






  def 							lowercase_     (						self						)				->					int:
     a   :	Optional[int]						=    CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate'						)
     a   :	Any						=    'Hugging Face大法好，谁用谁知道。'
     a   :	Tuple						=    'Hugging Face大法好,谁用谁知道。<unk>'
     a   :	Optional[int]						=    '▁Hu gg ing ▁ ▂ ▁F ace ▁大法 ▁好 ▁ , ▁谁 ▁用 ▁谁 ▁知 道 ▁ 。'.split()

     a   :	Optional[int]						=    tokenizer.tokenize(__UpperCAmelCase						)
     self.assertListEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)

     a   :	Optional[Any]						=    tokens + [tokenizer.unk_token]

     a   :	str						=    [1_37_89, 1_32_83, 14_21, 8, 10, 11_64, 1_36_08, 1_65_28, 63, 8, 9, 4_40, 1_08, 4_40, 1_21, 90, 8, 12, 0]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase						)  ,					__UpperCAmelCase						)

     a   :	List[Any]						=    tokenizer.decode(__UpperCAmelCase						)
     self.assertEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)





