




import numpy as np




class 			a__       :







     def __init__(	self  ,		A=5  ,		A=5 )  ->    List[str]:



          '''simple docstring'''



          a      =      depth
          a      =      0
          a      =      None
          a      =      None
          a      =      min_leaf_size
          a      =      None







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    Dict:



          '''simple docstring'''



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

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







     def 			lowerCAmelCase_				(	self  ,		A  ,		A )  ->    int:



          '''simple docstring'''



          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:
               a      =      np.mean(A )
               return

          if self.depth == 1:
               a      =      np.mean(A )
               return

          a      =      0
          a      =      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:
                    a      =      self.mean_squared_error(x[:i]  ,		np.mean(y[:i] ) )
                    a      =      self.mean_squared_error(x[i:]  ,		np.mean(y[i:] ) )
                    a      =      error_left + error_right
                    if error < min_error:
                         a      =      i
                         a      =      error

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

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

          return







     def 			lowerCAmelCase_				(	self  ,		A )  ->    Tuple:



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







class 			a__       :







     @staticmethod
     def 			lowerCAmelCase_				(	A  ,		A )  ->    Tuple:



          '''simple docstring'''



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

          return float(squared_error_sum / labels.size )







def 					SCREAMING_SNAKE_CASE							(   )			->     Tuple:
     a      =      np.arange(-1.0		,					1.0		,					0.005)
     a      =      np.sin(__UpperCamelCase)

     a      =      DecisionTree(depth=10		,					min_leaf_size=10)
     tree.train(__UpperCamelCase		,					__UpperCamelCase)

     a      =      (np.random.rand(10) * 2) - 1
     a      =      np.array([tree.predict(__UpperCamelCase) for x in test_cases])
     a      =      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)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

lowercase__      :			Optional[int]           =					{
    "uw-madison/yoso-4096": "https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}




class 			a__       (					UpperCamelCase__      ):
     a		:			Dict							=	"""yoso"""







     def __init__(	self  ,		A=50265  ,		A=768  ,		A=12  ,		A=12  ,		A=3072  ,		A="gelu"  ,		A=0.1  ,		A=0.1  ,		A=4096  ,		A=1  ,		A=0.0_2  ,		A=1e-12  ,		A="absolute"  ,		A=True  ,		A=9  ,		A=64  ,		A=None  ,		A=True  ,		A=True  ,		A=1  ,		A=0  ,		A=2  ,		**A  ,		)  ->    Dict:



          '''simple docstring'''



          super().__init__(pad_token_id=A  ,		bos_token_id=A  ,		eos_token_id=A  ,		**A )

          a      =      vocab_size
          a      =      max_position_embeddings
          a      =      hidden_size
          a      =      num_hidden_layers
          a      =      num_attention_heads
          a      =      intermediate_size
          a      =      hidden_act
          a      =      hidden_dropout_prob
          a      =      attention_probs_dropout_prob
          a      =      initializer_range
          a      =      type_vocab_size
          a      =      layer_norm_eps
          a      =      position_embedding_type
          a      =      use_expectation
          a      =      hash_code_len
          a      =      num_hash
          a      =      conv_window
          a      =      use_fast_hash
          a      =      lsh_backward

