code: 
  import json
  import os
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils import PreTrainedTokenizer
  from ...utils import logging
  
  
  __lowerCAmelCase				      =					logging.get_logger(__name__)
  
  __lowerCAmelCase				      =					{
      'vocab_file': 'vocab.json',
      'merges_file': 'merges.txt',
  }
  
  __lowerCAmelCase				      =					{
      'vocab_file': {
          'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json',
      },
      'merges_file': {'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'},
  }
  
  __lowerCAmelCase				      =					{
      'microsoft/biogpt': 1_0_2_4,
  }
  
  
  def       a			(   a  ):
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   set()
  			SCREAMING_SNAKE_CASE    	=   word[0]
  			for char in word[1:]:
  						pairs.add((prev_char, char)  )
  						SCREAMING_SNAKE_CASE    	=   char
  			return pairs
  
  
  
  
  
  class 						lowerCamelCase			(					__UpperCAmelCase				):
  	UpperCamelCase_     :				Dict								=  VOCAB_FILES_NAMES
  	UpperCamelCase_     :				Optional[int]								=  PRETRAINED_VOCAB_FILES_MAP
  	UpperCamelCase_     :				Tuple								=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  	UpperCamelCase_     :				List[Any]								=  ['input_ids', 'attention_mask']
  
  
  
  
  
  
  
  	def __init__(    self			:Union[str, Any]      ,    lowercase			:Optional[Any]      ,    lowercase			:Optional[int]      ,    lowercase			:List[str]="<unk>"      ,    lowercase			:str="<s>"      ,    lowercase			:Optional[Any]="</s>"      ,    lowercase			:Optional[int]="</s>"      ,    lowercase			:Union[str, Any]="<pad>"      ,    **lowercase			:Dict      ,    )		->			Optional[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				super().__init__(
  				    bos_token=lowercase_      ,    eos_token=lowercase_      ,    sep_token=lowercase_      ,    unk_token=lowercase_      ,    pad_token=lowercase_      ,    **lowercase_      ,    )
  
  				try:
  							import sacremoses
  				except ImportError:
  							raise ImportError(
  							    '''You need to install sacremoses to use BioGptTokenizer. '''
  							    '''See https://pypi.org/project/sacremoses/ for installation.''' )
  
  				SCREAMING_SNAKE_CASE    	=   '''en'''
  				SCREAMING_SNAKE_CASE    	=   sacremoses
  				# cache of sm.MosesTokenizer instance
  				SCREAMING_SNAKE_CASE    	=   {}
  				SCREAMING_SNAKE_CASE    	=   {}
  				with open(lowercase_      ,    encoding='''utf-8''' ) as vocab_handle:
  							SCREAMING_SNAKE_CASE    	=   json.load(lowercase_ )
  				SCREAMING_SNAKE_CASE    	=   {v: k for k, v in self.encoder.items()}
  				with open(lowercase_      ,    encoding='''utf-8''' ) as merges_handle:
  							SCREAMING_SNAKE_CASE    	=   merges_handle.read().split('''\n''' )[:-1]
  				SCREAMING_SNAKE_CASE    	=   [tuple(merge.split()[:2] ) for merge in merges]
  				SCREAMING_SNAKE_CASE    	=   dict(zip(lowercase_      ,    range(len(lowercase_ ) ) ) )
  				SCREAMING_SNAKE_CASE    	=   {}
  
  
  
  
  
  
  
  	@property
  	def    snake_case__    (    self			:Optional[int] )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				return len(self.encoder )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Any )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				return dict(self.encoder      ,    **self.added_tokens_encoder )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[Any]      ,    lowercase			:Dict      ,    lowercase			:int )		->			Optional[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if lang not in self.cache_moses_tokenizer:
  							SCREAMING_SNAKE_CASE    	=   self.sm.MosesTokenizer(lang=lowercase_ )
  							SCREAMING_SNAKE_CASE    	=   moses_tokenizer
  				return self.cache_moses_tokenizer[lang].tokenize(
  				    lowercase_      ,    aggressive_dash_splits=lowercase_      ,    return_str=lowercase_      ,    escape=lowercase_ )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:str      ,    lowercase			:Optional[Any]      ,    lowercase			:str )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if lang not in self.cache_moses_detokenizer:
  							SCREAMING_SNAKE_CASE    	=   self.sm.MosesDetokenizer(lang=lowercase_ )
  							SCREAMING_SNAKE_CASE    	=   moses_detokenizer
  				return self.cache_moses_detokenizer[lang].detokenize(lowercase_ )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[int]      ,    lowercase			:List[str] )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   tuple(token[:-1] ) + (token[-1] + '''</w>''',)
  				if token in self.cache:
  							return self.cache[token]
  				SCREAMING_SNAKE_CASE    	=   get_pairs(lowercase_ )
  
  				if not pairs:
  							return token + "</w>"
  
  				while True:
  							SCREAMING_SNAKE_CASE    	=   min(lowercase_      ,    key=lambda lowercase : self.bpe_ranks.get(lowercase_      ,    float('''inf''' ) ) )
  							if bigram not in self.bpe_ranks:
  										break
  							SCREAMING_SNAKE_CASE    	=   bigram
  							SCREAMING_SNAKE_CASE    	=   []
  							SCREAMING_SNAKE_CASE    	=   0
  							while i < len(lowercase_ ):
  										try:
  													SCREAMING_SNAKE_CASE    	=   word.index(lowercase_      ,    lowercase_ )
  										except ValueError:
  													new_word.extend(word[i:] )
  													break
  										else:
  													new_word.extend(word[i:j] )
  													SCREAMING_SNAKE_CASE    	=   j
  
  										if word[i] == first and i < len(lowercase_ ) - 1 and word[i + 1] == second:
  													new_word.append(first + second )
  													i += 2
  										else:
  													new_word.append(word[i] )
  													i += 1
  							SCREAMING_SNAKE_CASE    	=   tuple(lowercase_ )
  							SCREAMING_SNAKE_CASE    	=   new_word
  							if len(lowercase_ ) == 1:
  										break
  							else:
  										SCREAMING_SNAKE_CASE    	=   get_pairs(lowercase_ )
  				SCREAMING_SNAKE_CASE    	=   ''' '''.join(lowercase_ )
  				if word == "\n  </w>":
  							SCREAMING_SNAKE_CASE    	=   '''\n</w>'''
  				SCREAMING_SNAKE_CASE    	=   word
  				return word
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[Any]      ,    lowercase			:Dict      ,    lowercase			:List[str]=False )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if bypass_tokenizer:
  							SCREAMING_SNAKE_CASE    	=   text.split()
  				else:
  							SCREAMING_SNAKE_CASE    	=   self.moses_tokenize(lowercase_      ,    self.lang )
  
  				SCREAMING_SNAKE_CASE    	=   []
  				for token in text:
  							if token:
  										split_tokens.extend(list(self.bpe(lowercase_ ).split(''' ''' ) ) )
  
  				return split_tokens
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[str]      ,    lowercase			:List[str] )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				return self.encoder.get(lowercase_      ,    self.encoder.get(self.unk_token ) )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[Any]      ,    lowercase			:Optional[Any] )		->			Optional[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				return self.decoder.get(lowercase_      ,    self.unk_token )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[Any]      ,    lowercase			:Union[str, Any] )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   [t.replace(''' '''      ,    '''''' ).replace('''</w>'''      ,    ''' ''' ) for t in tokens]
  				SCREAMING_SNAKE_CASE    	=   ''''''.join(lowercase_ ).split()
  				# detokenize
  				SCREAMING_SNAKE_CASE    	=   self.moses_detokenize(lowercase_      ,    self.lang )
  				return text
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[Any]      ,    lowercase			:List[Any]      ,    lowercase			:List[Any] = None )		->			List[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if token_ids_a is None:
  							return [self.sep_token_id] + token_ids_a
  				SCREAMING_SNAKE_CASE    	=   [self.sep_token_id]
  				return sep + token_ids_a + sep + token_ids_a
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[int]      ,    lowercase			:List[Any]      ,    lowercase			:Optional[Any] = None      ,    lowercase			:Optional[Any] = False )		->			List[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if already_has_special_tokens:
  							return super().get_special_tokens_mask(
  							    token_ids_a=lowercase_      ,    token_ids_a=lowercase_      ,    already_has_special_tokens=lowercase_ )
  				# no bos used in fairseq
  				if token_ids_a is not None:
  							return [1] + ([0] * len(lowercase_ )) + [1] + ([0] * len(lowercase_ ))
  				return [1] + ([0] * len(lowercase_ ))
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Any      ,    lowercase			:Optional[Any]      ,    lowercase			:Optional[Any] = None )		->			List[int]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   [self.sep_token_id]
  
  				# no bos used in fairseq
  				if token_ids_a is None:
  							return len(token_ids_a + sep ) * [0]
  				return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Dict      ,    lowercase			:int      ,    lowercase			:List[str] = None )		->			Tuple[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				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'''] )
  				SCREAMING_SNAKE_CASE    	=   os.path.join(
  				    lowercase_      ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
  
  				with open(lowercase_      ,    '''w'''      ,    encoding='''utf-8''' ) as f:
  							f.write(json.dumps(self.encoder      ,    indent=2      ,    sort_keys=lowercase_      ,    ensure_ascii=lowercase_ ) + '''\n''' )
  
  				SCREAMING_SNAKE_CASE    	=   0
  				with open(lowercase_      ,    '''w'''      ,    encoding='''utf-8''' ) as writer:
  							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      ,    key=lambda lowercase : kv[1] ):
  										if index != token_index:
  													logger.warning(
  													    f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
  													    ''' Please check that the tokenizer is not corrupted!''' )
  													SCREAMING_SNAKE_CASE    	=   token_index
  										writer.write(''' '''.join(lowercase_ ) + '''\n''' )
  										index += 1
  
  				return vocab_file, merge_file
  
  
  
  
  
  
  
  	def __getstate__(    self			:Any )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   self.__dict__.copy()
  				SCREAMING_SNAKE_CASE    	=   None
  				return state
  
  
  
  
  
  
  
  	def __setstate__(    self			:Optional[Any]      ,    lowercase			:List[Any] )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   d
  
  				try:
  							import sacremoses
  				except ImportError:
  							raise ImportError(
  							    '''You need to install sacremoses to use XLMTokenizer. '''
  							    '''See https://pypi.org/project/sacremoses/ for installation.''' )
  
  				SCREAMING_SNAKE_CASE    	=   sacremoses
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)