code: 
  
  """simple docstring"""
  
  
  
  
  
  
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
    from .tokenization_xlm_roberta import XLMRobertaTokenizer
  else:
    SCREAMING_SNAKE_CASE_     				=   None
  
  
  SCREAMING_SNAKE_CASE_     				=   logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE_     				=   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}
  
  SCREAMING_SNAKE_CASE_     				=   {
      'vocab_file': {
          'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
          'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
          'xlm-roberta-large-finetuned-conll02-dutch': (
              'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
          ),
          'xlm-roberta-large-finetuned-conll02-spanish': (
              'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
          ),
          'xlm-roberta-large-finetuned-conll03-english': (
              'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
          ),
          'xlm-roberta-large-finetuned-conll03-german': (
              'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
          ),
      },
      'tokenizer_file': {
          'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json',
          'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json',
          'xlm-roberta-large-finetuned-conll02-dutch': (
              'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'
          ),
          'xlm-roberta-large-finetuned-conll02-spanish': (
              'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'
          ),
          'xlm-roberta-large-finetuned-conll03-english': (
              'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'
          ),
          'xlm-roberta-large-finetuned-conll03-german': (
              'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'
          ),
      },
  }
  
  SCREAMING_SNAKE_CASE_     				=   {
      'xlm-roberta-base': 512,
      'xlm-roberta-large': 512,
      'xlm-roberta-large-finetuned-conll02-dutch': 512,
      'xlm-roberta-large-finetuned-conll02-spanish': 512,
      'xlm-roberta-large-finetuned-conll03-english': 512,
      'xlm-roberta-large-finetuned-conll03-german': 512,
  }
  
  
  
  
  
  
  class     __a			(					lowerCamelCase__			):
         _lowerCamelCase							:							str       =			VOCAB_FILES_NAMES
         _lowerCamelCase							:							Dict       =			PRETRAINED_VOCAB_FILES_MAP
         _lowerCamelCase							:							Union[str, Any]       =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
         _lowerCamelCase							:							List[str]       =			['input_ids', 'attention_mask']
         _lowerCamelCase							:							Optional[Any]       =			XLMRobertaTokenizer
  
  
  
  
  
  
  
         def __init__(					self							,lowerCamelCase_=None							,lowerCamelCase_=None							,lowerCamelCase_="<s>"							,lowerCamelCase_="</s>"							,lowerCamelCase_="</s>"							,lowerCamelCase_="<s>"							,lowerCamelCase_="<unk>"							,lowerCamelCase_="<pad>"							,lowerCamelCase_="<mask>"							,**lowerCamelCase_							,):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					AddedToken(SCREAMING_SNAKE_CASE_							,lstrip=SCREAMING_SNAKE_CASE_							,rstrip=SCREAMING_SNAKE_CASE_		) if isinstance(SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_		) else mask_token
  
            super().__init__(
                SCREAMING_SNAKE_CASE_							,tokenizer_file=SCREAMING_SNAKE_CASE_							,bos_token=SCREAMING_SNAKE_CASE_							,eos_token=SCREAMING_SNAKE_CASE_							,sep_token=SCREAMING_SNAKE_CASE_							,cls_token=SCREAMING_SNAKE_CASE_							,unk_token=SCREAMING_SNAKE_CASE_							,pad_token=SCREAMING_SNAKE_CASE_							,mask_token=SCREAMING_SNAKE_CASE_							,**SCREAMING_SNAKE_CASE_							,)
  
            _UpperCAmelCase   	=					vocab_file
            _UpperCAmelCase   	=					False if not self.vocab_file else True
  
  
  
  
  
  
  
         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]
            _UpperCAmelCase   	=					[self.cls_token_id]
            _UpperCAmelCase   	=					[self.sep_token_id]
            return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_ = None		):
  
  
  
            '''simple docstring'''
  
            _UpperCAmelCase   	=					[self.sep_token_id]
            _UpperCAmelCase   	=					[self.cls_token_id]
  
            if token_ids_a is None:
               return len(cls + token_ids_a + sep		) * [0]
            return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]
  
  
  
  
  
  
  
         def    _snake_case			(					self							,lowerCamelCase_							,lowerCamelCase_ = None		):
  
  
  
            '''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(SCREAMING_SNAKE_CASE_		):
               logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''		)
               return
            _UpperCAmelCase   	=					os.path.join(
                SCREAMING_SNAKE_CASE_							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
  
            if os.path.abspath(self.vocab_file		) != os.path.abspath(SCREAMING_SNAKE_CASE_		):
               copyfile(self.vocab_file							,SCREAMING_SNAKE_CASE_		)
  
            return (out_vocab_file,)
  
  
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)
  
  