code: 
  
  """simple docstring"""
  
  
  
  
  
  
  
  from typing import Dict, List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  SCREAMING_SNAKE_CASE_     				=   logging.get_logger(__name__)
  
  
  
  
  
  
  class     __a			(					lowerCAmelCase_			):
         _lowerCamelCase							:							Optional[int]       =			['input_ids', 'attention_mask']
  
  
  
  
  
  
  
         def __init__(					self							,lowerCamelCase_="[PAD]"							,lowerCamelCase_="[BOS]"							,lowerCamelCase_="[EOS]"							,lowerCamelCase_="[MASK]"							,lowerCamelCase_="[CLS]"							,lowerCamelCase_="[SEP]"							,lowerCamelCase_=2048							,**lowerCamelCase_							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					AddedToken(lowerCamelCase_							,lstrip=lowerCamelCase_							,rstrip=lowerCamelCase_		) if isinstance(lowerCamelCase_							,lowerCamelCase_		) else pad_token
            _UpperCAmelCase   	=					AddedToken(lowerCamelCase_							,lstrip=lowerCamelCase_							,rstrip=lowerCamelCase_		) if isinstance(lowerCamelCase_							,lowerCamelCase_		) else bos_token
            _UpperCAmelCase   	=					AddedToken(lowerCamelCase_							,lstrip=lowerCamelCase_							,rstrip=lowerCamelCase_		) if isinstance(lowerCamelCase_							,lowerCamelCase_		) else eos_token
            _UpperCAmelCase   	=					AddedToken(lowerCamelCase_							,lstrip=lowerCamelCase_							,rstrip=lowerCamelCase_		) if isinstance(lowerCamelCase_							,lowerCamelCase_		) else mask_token
            _UpperCAmelCase   	=					AddedToken(lowerCamelCase_							,lstrip=lowerCamelCase_							,rstrip=lowerCamelCase_		) if isinstance(lowerCamelCase_							,lowerCamelCase_		) else cls_token
            _UpperCAmelCase   	=					AddedToken(lowerCamelCase_							,lstrip=lowerCamelCase_							,rstrip=lowerCamelCase_		) if isinstance(lowerCamelCase_							,lowerCamelCase_		) else sep_token
  
            super().__init__(
                pad_token=lowerCamelCase_							,bos_token=lowerCamelCase_							,eos_token=lowerCamelCase_							,mask_token=lowerCamelCase_							,cls_token=lowerCamelCase_							,sep_token=lowerCamelCase_							,model_max_length=lowerCamelCase_							,**lowerCamelCase_							,)
  
            _UpperCAmelCase   	=					2**8  # utf is 8 bits
  
            # define special tokens dict
            _UpperCAmelCase   	=					{
                self.pad_token: 0,
                self.bos_token: 1,
                self.eos_token: 2,
                self.mask_token: 3,
                self.cls_token: 4,
                self.sep_token: 5,
            }
            _UpperCAmelCase   	=					len(self.special_tokens_encoder		)
            _UpperCAmelCase   	=					{v: k for k, v in self.special_tokens_encoder.items()}
  
  
  
  
  
  
  
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					self.special_tokens_encoder.copy()
            vocab.update(self.added_tokens_encoder		)
            for i in range(self._utf_vocab_size		):
               _UpperCAmelCase   	=					chr(lowerCamelCase_		)
               _UpperCAmelCase   	=					i + len(self.special_tokens_encoder		)
            return vocab
  
  
  
  
  
  
  
         @property
         def    _snake_case			(					self		):
  
  
  
            '''simple docstring'''
  
            return self._utf_vocab_size + self._num_special_tokens
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_ = None							,lowerCamelCase_ = False		):
  
  
  
            '''simple docstring'''
  
            if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCamelCase_							,token_ids_a=lowerCamelCase_							,already_has_special_tokens=lowerCamelCase_		)
  
            # normal case: some special tokens
            if token_ids_a is None:
               return [1] + [0] * len(lowerCamelCase_		) + [1]
            return [1] + ([0] * len(lowerCamelCase_		)) + [1] + ([0] * len(lowerCamelCase_		)) + [1]
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_ = None		):
  
  
  
            '''simple docstring'''
  
            if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            else:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					[chr(lowerCamelCase_		) for i in text.encode("utf-8"		)]
            return tokens
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            if token in self.special_tokens_encoder:
               _UpperCAmelCase   	=					self.special_tokens_encoder[token]
            elif token in self.added_tokens_encoder:
               _UpperCAmelCase   	=					self.added_tokens_encoder[token]
            elif len(lowerCamelCase_		) != 1:
               _UpperCAmelCase   	=					self.unk_token_id
            else:
               _UpperCAmelCase   	=					ord(lowerCamelCase_		) + self._num_special_tokens
            return token_id
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            if index in self.special_tokens_decoder:
               _UpperCAmelCase   	=					self.special_tokens_decoder[index]
            elif index in self.added_tokens_decoder:
               _UpperCAmelCase   	=					self.added_tokens_decoder[index]
            else:
               _UpperCAmelCase   	=					chr(index - self._num_special_tokens		)
            return token
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					b""
            for token in tokens:
               if token in self.special_tokens_decoder:
                  _UpperCAmelCase   	=					self.special_tokens_decoder[token].encode("utf-8"		)
               elif token in self.added_tokens_decoder:
                  _UpperCAmelCase   	=					self.special_tokens_decoder[token].encode("utf-8"		)
               elif token in self.special_tokens_encoder:
                  _UpperCAmelCase   	=					token.encode("utf-8"		)
               elif token in self.added_tokens_encoder:
                  _UpperCAmelCase   	=					token.encode("utf-8"		)
               else:
                  _UpperCAmelCase   	=					bytes([ord(lowerCamelCase_		)]		)
               bstring += tok_string
            _UpperCAmelCase   	=					bstring.decode("utf-8"							,errors="replace"		)
            return string
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_ = None		):
  
  
  
            '''simple docstring'''
  
            return ()
  
  
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)
  
  