code: 
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
  from ...utils import logging
  
  
  __lowerCAmelCase				      =					logging.get_logger(__name__)
  
  __lowerCAmelCase				      =					{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}
  
  __lowerCAmelCase				      =					{
      'vocab_file': {
          'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
      },
      'tokenizer_file': {
          'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json',
      },
  }
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  
  
  
  
  
  
  
  	def __init__(    self			:Any      ,    lowercase			:List[Any]=None      ,    lowercase			:Optional[Any]=None      ,    lowercase			:Optional[int]=False      ,    lowercase			:List[str]=True      ,    lowercase			:Any=False      ,    lowercase			:str="<s>"      ,    lowercase			:Tuple="</s>"      ,    lowercase			:Optional[int]="<unk>"      ,    lowercase			:Any="<sep>"      ,    lowercase			:Dict="<pad>"      ,    lowercase			:Tuple="<cls>"      ,    lowercase			:str="<mask>"      ,    lowercase			:int=["<eop>", "<eod>"]      ,    **lowercase			:Dict      ,    )		->			Optional[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   AddedToken(lowercase      ,    lstrip=lowercase      ,    rstrip=lowercase ) if isinstance(lowercase      ,    lowercase ) else mask_token
  
  				super().__init__(
  				    vocab_file=lowercase      ,    tokenizer_file=lowercase      ,    do_lower_case=lowercase      ,    remove_space=lowercase      ,    keep_accents=lowercase      ,    bos_token=lowercase      ,    eos_token=lowercase      ,    unk_token=lowercase      ,    sep_token=lowercase      ,    pad_token=lowercase      ,    cls_token=lowercase      ,    mask_token=lowercase      ,    additional_special_tokens=lowercase      ,    **lowercase      ,    )
  
  				SCREAMING_SNAKE_CASE    	=   3
  				SCREAMING_SNAKE_CASE    	=   do_lower_case
  				SCREAMING_SNAKE_CASE    	=   remove_space
  				SCREAMING_SNAKE_CASE    	=   keep_accents
  				SCREAMING_SNAKE_CASE    	=   vocab_file
  				SCREAMING_SNAKE_CASE    	=   False if not self.vocab_file else True
  
  				try:
  							import jieba
  				except ModuleNotFoundError as error:
  							raise error.__class__(
  							    '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
  							    '''See https://pypi.org/project/jieba/ for installation.''' )
  				SCREAMING_SNAKE_CASE    	=   jieba
  				SCREAMING_SNAKE_CASE    	=   str.maketrans(''' \n'''      ,    '''\u2582\u2583''' )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Union[str, Any]      ,    lowercase			:List[int]      ,    lowercase			:Optional[List[int]] = None )		->			List[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   [self.sep_token_id]
  				SCREAMING_SNAKE_CASE    	=   [self.cls_token_id]
  				if token_ids_a is None:
  							return token_ids_a + sep + cls
  				return token_ids_a + sep + token_ids_a + sep + cls
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:int      ,    lowercase			:List[int]      ,    lowercase			:Optional[List[int]] = None )		->			List[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   [self.sep_token_id]
  				SCREAMING_SNAKE_CASE    	=   [2]
  
  				if token_ids_a is None:
  							return len(token_ids_a + sep ) * [0] + cls_segment_id
  				return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[str]      ,    lowercase			:str      ,    lowercase			:Optional[str] = None )		->			Tuple[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if not self.can_save_slow_tokenizer:
  							raise ValueError(
  							    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
  							    '''tokenizer.''' )
  
  				if not os.path.isdir(lowercase ):
  							logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
  							return
  				SCREAMING_SNAKE_CASE    	=   os.path.join(
  				    lowercase      ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
  
  				if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase ):
  							copyfile(self.vocab_file      ,    lowercase )
  
  				return (out_vocab_file,)
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Dict      ,    lowercase			:int      ,    *lowercase			:Any      ,    **lowercase			:int )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   [
  				    ''' '''.join([x.translate(self.translator ) for x in self.jieba.cut(lowercase      ,    cut_all=lowercase )] )
  				    for text in batch_text_or_text_pairs
  				]
  				return super()._batch_encode_plus(lowercase      ,    *lowercase      ,    **lowercase )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[Any]      ,    *lowercase			:Optional[Any]      ,    **lowercase			:Optional[int] )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   super()._decode(*lowercase      ,    **lowercase )
  				SCREAMING_SNAKE_CASE    	=   text.replace(''' '''      ,    '''''' ).replace('''\u2582'''      ,    ''' ''' ).replace('''\u2583'''      ,    '''\n''' )
  				return text
style_context: 
  import numpy as np
  
  
  
  
  
  class 						lowerCamelCase			:
  
  
  
  
  
  
  
  	def __init__(    self			:Any      ,    lowercase			:List[Any]=5      ,    lowercase			:Optional[Any]=5 )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   depth
  				SCREAMING_SNAKE_CASE    	=   0
  				SCREAMING_SNAKE_CASE    	=   None
  				SCREAMING_SNAKE_CASE    	=   None
  				SCREAMING_SNAKE_CASE    	=   min_leaf_size
  				SCREAMING_SNAKE_CASE    	=   None
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:str      ,    lowercase			:int      ,    lowercase			:int )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if labels.ndim != 1:
  							print('''Error: Input labels must be one dimensional''' )
  
  				return np.mean((labels - prediction) ** 2 )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Union[str, Any]      ,    lowercase			:int      ,    lowercase			:Dict )		->			Tuple:
  
  
  
  
  
  
  
  				"""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:
  							SCREAMING_SNAKE_CASE    	=   np.mean(lowercase )
  							return
  
  				if self.depth == 1:
  							SCREAMING_SNAKE_CASE    	=   np.mean(lowercase )
  							return
  
  				SCREAMING_SNAKE_CASE    	=   0
  				SCREAMING_SNAKE_CASE    	=   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:
  										SCREAMING_SNAKE_CASE    	=   self.mean_squared_error(x[:i]      ,    np.mean(y[:i] ) )
  										SCREAMING_SNAKE_CASE    	=   self.mean_squared_error(x[i:]      ,    np.mean(y[i:] ) )
  										SCREAMING_SNAKE_CASE    	=   error_left + error_right
  										if error < min_error:
  													SCREAMING_SNAKE_CASE    	=   i
  													SCREAMING_SNAKE_CASE    	=   error
  
  				if best_split != 0:
  							SCREAMING_SNAKE_CASE    	=   x[:best_split]
  							SCREAMING_SNAKE_CASE    	=   y[:best_split]
  							SCREAMING_SNAKE_CASE    	=   x[best_split:]
  							SCREAMING_SNAKE_CASE    	=   y[best_split:]
  
  							SCREAMING_SNAKE_CASE    	=   x[best_split]
  							SCREAMING_SNAKE_CASE    	=   DecisionTree(
  							    depth=self.depth - 1      ,    min_leaf_size=self.min_leaf_size )
  							SCREAMING_SNAKE_CASE    	=   DecisionTree(
  							    depth=self.depth - 1      ,    min_leaf_size=self.min_leaf_size )
  							self.left.train(lowercase      ,    lowercase )
  							self.right.train(lowercase      ,    lowercase )
  				else:
  							SCREAMING_SNAKE_CASE    	=   np.mean(lowercase )
  
  				return
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[int]      ,    lowercase			:List[Any] )		->			str:
  
  
  
  
  
  
  
  				"""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 						lowerCamelCase			:
  
  
  
  
  
  
  
  	@staticmethod
  	def    snake_case__    (    lowercase			:Optional[int]      ,    lowercase			:Optional[Any] )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   float(0 )
  				for label in labels:
  							squared_error_sum += (label - prediction) ** 2
  
  				return float(squared_error_sum / labels.size )
  
  
  
  def       a			(   )    ->Any:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   np.arange(-1.0		,	1.0		,	0.0_05  )
  			SCREAMING_SNAKE_CASE    	=   np.sin(a  )
  
  			SCREAMING_SNAKE_CASE    	=   DecisionTree(depth=10		,	min_leaf_size=10  )
  			tree.train(a		,	a  )
  
  			SCREAMING_SNAKE_CASE    	=   (np.random.rand(10  ) * 2) - 1
  			SCREAMING_SNAKE_CASE    	=   np.array([tree.predict(a  ) for x in test_cases]  )
  			SCREAMING_SNAKE_CASE    	=   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)