code: 
  import json
  import os
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils import PreTrainedTokenizer
  from ...utils import logging
  
  
  snake_case				:	Any							   =						logging.get_logger(__name__)
  
  snake_case				:	Optional[int]							   =						{
      "vocab_file": "vocab.json",
      "merges_file": "merges.txt",
  }
  
  snake_case				:	List[str]							   =						{
      "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"},
  }
  
  snake_case				:	Optional[Any]							   =						{
      "microsoft/biogpt": 1_024,
  }
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						Optional[int]     )       ->	Any:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	int			     =  set()
  	__magic_name__	:	str			     =  word[0]
  	for char in word[1:]:
  		pairs.add((prev_char, char)     )
  		__magic_name__	:	Optional[int]			     =  char
  	return pairs
  
  
  
  
  
  class    _snake_case      (  snake_case       ):
  			UpperCamelCase__					   =					VOCAB_FILES_NAMES
  			UpperCamelCase__					   =					PRETRAINED_VOCAB_FILES_MAP
  			UpperCamelCase__					   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  			UpperCamelCase__					   =					['input_ids', 'attention_mask']
  
  
  
  
  
  
  
  			def __init__(       self		,     _a		,     _a		,     _a="<unk>"		,     _a="<s>"		,     _a="</s>"		,     _a="</s>"		,     _a="<pad>"		,     **_a		,     ):
  				super().__init__(
  				    bos_token=_a		,     eos_token=_a		,     sep_token=_a		,     unk_token=_a		,     pad_token=_a		,     **_a		,     )
  
  				try:
  					import sacremoses
  				except ImportError:
  					raise ImportError(
  					    "You need to install sacremoses to use BioGptTokenizer. "
  					    "See https://pypi.org/project/sacremoses/ for installation."	)
  
  				__magic_name__	:	Optional[int]			     =  "en"
  				__magic_name__	:	Optional[Any]			     =  sacremoses
  				# cache of sm.MosesTokenizer instance
  				__magic_name__	:	int			     =  {}
  				__magic_name__	:	str			     =  {}
  				with open(_a		,     encoding="utf-8"	) as vocab_handle:
  					__magic_name__	:	Any			     =  json.load(_a	)
  				__magic_name__	:	List[Any]			     =  {v: k for k, v in self.encoder.items()}
  				with open(_a		,     encoding="utf-8"	) as merges_handle:
  					__magic_name__	:	Optional[int]			     =  merges_handle.read().split("\n"	)[:-1]
  				__magic_name__	:	Any			     =  [tuple(merge.split()[:2]	) for merge in merges]
  				__magic_name__	:	Tuple			     =  dict(zip(_a		,     range(len(_a	)	)	)	)
  				__magic_name__	:	List[str]			     =  {}
  
  
  
  
  
  
  
  			@property
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return len(self.encoder	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				return dict(self.encoder		,     **self.added_tokens_encoder	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a	):
  				if lang not in self.cache_moses_tokenizer:
  					__magic_name__	:	Optional[int]			     =  self.sm.MosesTokenizer(lang=_a	)
  					__magic_name__	:	Optional[Any]			     =  moses_tokenizer
  				return self.cache_moses_tokenizer[lang].tokenize(
  				    _a		,     aggressive_dash_splits=_a		,     return_str=_a		,     escape=_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a	):
  				if lang not in self.cache_moses_detokenizer:
  					__magic_name__	:	List[Any]			     =  self.sm.MosesDetokenizer(lang=_a	)
  					__magic_name__	:	Any			     =  moses_detokenizer
  				return self.cache_moses_detokenizer[lang].detokenize(_a	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				__magic_name__	:	List[Any]			     =  tuple(token[:-1]	) + (token[-1] + "</w>",)
  				if token in self.cache:
  					return self.cache[token]
  				__magic_name__	:	int			     =  get_pairs(_a	)
  
  				if not pairs:
  					return token + "</w>"
  
  				while True:
  					__magic_name__	:	Union[str, Any]			     =  min(_a		,     key=lambda _a	: self.bpe_ranks.get(_a		,     float("inf"	)	)	)
  					if bigram not in self.bpe_ranks:
  						break
  					__magic_name__     ,					__magic_name__	:	Any			     =  bigram
  					__magic_name__	:	Any			     =  []
  					__magic_name__	:	Dict			     =  0
  					while i < len(_a	):
  						try:
  							__magic_name__	:	List[Any]			     =  word.index(_a		,     _a	)
  						except ValueError:
  							new_word.extend(word[i:]	)
  							break
  						else:
  							new_word.extend(word[i:j]	)
  							__magic_name__	:	List[Any]			     =  j
  
  						if word[i] == first and i < len(_a	) - 1 and word[i + 1] == second:
  							new_word.append(first + second	)
  							i += 2
  						else:
  							new_word.append(word[i]	)
  							i += 1
  					__magic_name__	:	str			     =  tuple(_a	)
  					__magic_name__	:	str			     =  new_word
  					if len(_a	) == 1:
  						break
  					else:
  						__magic_name__	:	Dict			     =  get_pairs(_a	)
  				__magic_name__	:	Tuple			     =  " ".join(_a	)
  				if word == "\n  </w>":
  					__magic_name__	:	Any			     =  "\n</w>"
  				__magic_name__	:	Tuple			     =  word
  				return word
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a=False	):
  				if bypass_tokenizer:
  					__magic_name__	:	Dict			     =  text.split()
  				else:
  					__magic_name__	:	Any			     =  self.moses_tokenize(_a		,     self.lang	)
  
  				__magic_name__	:	Dict			     =  []
  				for token in text:
  					if token:
  						split_tokens.extend(list(self.bpe(_a	).split(" "	)	)	)
  
  				return split_tokens
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				return self.encoder.get(_a		,     self.encoder.get(self.unk_token	)	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				return self.decoder.get(_a		,     self.unk_token	)
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  				__magic_name__	:	Any			     =  [t.replace(" "		,     ""	).replace("</w>"		,     " "	) for t in tokens]
  				__magic_name__	:	Optional[Any]			     =  "".join(_a	).split()
  				# detokenize
  				__magic_name__	:	List[Any]			     =  self.moses_detokenize(_a		,     self.lang	)
  				return text
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  				if token_ids_a is None:
  					return [self.sep_token_id] + token_ids_a
  				__magic_name__	:	str			     =  [self.sep_token_id]
  				return sep + token_ids_a + sep + token_ids_a
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None		,     _a = False	):
  				if already_has_special_tokens:
  					return super().get_special_tokens_mask(
  					    token_ids_a=_a		,     token_ids_a=_a		,     already_has_special_tokens=_a	)
  				# no bos used in fairseq
  				if token_ids_a is not None:
  					return [1] + ([0] * len(_a	)) + [1] + ([0] * len(_a	))
  				return [1] + ([0] * len(_a	))
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  				__magic_name__	:	Dict			     =  [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     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
  				if not os.path.isdir(_a	):
  					logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
  					return
  				__magic_name__	:	str			     =  os.path.join(
  				    _a		,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
  				__magic_name__	:	List[Any]			     =  os.path.join(
  				    _a		,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]	)
  
  				with open(_a		,     "w"		,     encoding="utf-8"	) as f:
  					f.write(json.dumps(self.encoder		,     indent=2		,     sort_keys=_a		,     ensure_ascii=_a	) + "\n"	)
  
  				__magic_name__	:	Optional[int]			     =  0
  				with open(_a		,     "w"		,     encoding="utf-8"	) as writer:
  					for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,     key=lambda _a	: 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!"	)
  							__magic_name__	:	int			     =  token_index
  						writer.write(" ".join(_a	) + "\n"	)
  						index += 1
  
  				return vocab_file, merge_file
  
  
  
  
  
  
  
  			def __getstate__(       self	):
  				__magic_name__	:	str			     =  self.__dict__.copy()
  				__magic_name__	:	List[Any]			     =  None
  				return state
  
  
  
  
  
  
  
  			def __setstate__(       self		,     _a	):
  				__magic_name__	:	Optional[Any]			     =  d
  
  				try:
  					import sacremoses
  				except ImportError:
  					raise ImportError(
  					    "You need to install sacremoses to use XLMTokenizer. "
  					    "See https://pypi.org/project/sacremoses/ for installation."	)
  
  				__magic_name__	:	Tuple			     =  sacremoses
  
style_context: 
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  snake_case				:	Optional[int]							   =						{
      "configuration_mobilevitv2": [
          "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "MobileViTV2Config",
          "MobileViTV2OnnxConfig",
      ],
  }
  
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				snake_case				:	Optional[int]							   =						[
  				    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "MobileViTV2ForImageClassification",
  				    "MobileViTV2ForSemanticSegmentation",
  				    "MobileViTV2Model",
  				    "MobileViTV2PreTrainedModel",
  				]
  
  if TYPE_CHECKING:
  				from .configuration_mobilevitva import (
  				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
  				    MobileViTVaConfig,
  				    MobileViTVaOnnxConfig,
  				)
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_mobilevitva import (
  								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    MobileViTVaForImageClassification,
  								    MobileViTVaForSemanticSegmentation,
  								    MobileViTVaModel,
  								    MobileViTVaPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				snake_case				:	Tuple							   =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  