



a	:   Optional[int]       			=       8.3_1_4_4_5_9_8







def       lowerCAmelCase_     (lowerCAmelCase__:						float						,		lowerCAmelCase__:						float   ):





			"""simple docstring"""


			if temperature < 0:
						raise Exception("""Temperature cannot be less than 0 K"""   )
			if molar_mass <= 0:
						raise Exception("""Molar mass cannot be less than or equal to 0 kg/mol"""   )
			else:
						return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
	import doctest

	# run doctest
	doctest.testmod()

	# example
	a	:   Optional[Any]       			=       300
	a	:   Optional[int]       			=       28
	a	:   Any       			=       rms_speed_of_molecule(temperature, molar_mass)
	print(F'''Vrms of Nitrogen gas at 300 K is {vrms} m/s''')








'''simple docstring'''







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase         =    logging.get_logger(__name__)

__lowerCAmelCase         =    """▁"""

__lowerCAmelCase         =    {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

__lowerCAmelCase         =    {
    """vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
    },
    """monolingual_vocab_file""": {
        """vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
    },
}

__lowerCAmelCase         =    {"""vinai/bartpho-syllable""": 1_0_2_4}







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				Optional[Any]   =					VOCAB_FILES_NAMES
  __UpperCAmelCase				:				Dict   =					PRETRAINED_VOCAB_FILES_MAP
  __UpperCAmelCase				:				Optional[int]   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __UpperCAmelCase				:				Dict   =					['''input_ids''', '''attention_mask''']


  def __init__(		self      :     str							,_a      :     str							,_a      :     Any							,_a      :     Any="<s>"							,_a      :     Dict="</s>"							,_a      :     int="</s>"							,_a      :     Union[str, Any]="<s>"							,_a      :     List[Any]="<unk>"							,_a      :     Optional[Any]="<pad>"							,_a      :     List[str]="<mask>"							,_a      :     Optional[Dict[str, Any]] = None							,**_a      :     int							,):


     '''simple docstring'''






     _a				:	Any			=   AddedToken(_a							,lstrip=_a							,rstrip=_a       ) if isinstance(_a							,_a       ) else mask_token

     _a				:	Optional[Any]			=   {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         bos_token=_a							,eos_token=_a							,unk_token=_a							,sep_token=_a							,cls_token=_a							,pad_token=_a							,mask_token=_a							,sp_model_kwargs=self.sp_model_kwargs							,**_a							,)

     _a				:	Optional[int]			=   vocab_file
     _a				:	Union[str, Any]			=   monolingual_vocab_file
     _a				:	List[Any]			=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
     self.sp_model.Load(str(_a       )       )

     # Load the reduced vocab

     # Keep order of special tokens for backward compatibility
     _a				:	Union[str, Any]			=   {}
     _a				:	int			=   0
     for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
        if str(_a       ) not in self.fairseq_tokens_to_ids:
           _a				:	int			=   cnt
           cnt += 1
     with open(_a							,'r'							,encoding='utf-8'       ) as f:
        for line in f.readlines():
           _a				:	str			=   line.strip().split()[0]
           _a				:	Tuple			=   len(self.fairseq_tokens_to_ids       )
     if str(_a       ) not in self.fairseq_tokens_to_ids:
        _a				:	List[str]			=   len(self.fairseq_tokens_to_ids       )

     _a				:	Tuple			=   {v: k for k, v in self.fairseq_tokens_to_ids.items()}


  def __getstate__(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	int			=   self.__dict__.copy()
     _a				:	str			=   None
     _a				:	Optional[Any]			=   self.sp_model.serialized_model_proto()
     return state


  def __setstate__(		self      :     Tuple							,_a      :     Tuple       ):


     '''simple docstring'''






     _a				:	Tuple			=   d

     # for backward compatibility
     if not hasattr(self							,'sp_model_kwargs'       ):
        _a				:	List[str]			=   {}

     _a				:	List[Any]			=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
     self.sp_model.LoadFromSerializedProto(self.sp_model_proto       )


  def   __lowercase		(		self      :     Dict							,_a      :     List[int]							,_a      :     Optional[List[int]] = None       ):


     '''simple docstring'''






     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     _a				:	Dict			=   [self.cls_token_id]
     _a				:	int			=   [self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep


  def   __lowercase		(		self      :     int							,_a      :     List[int]							,_a      :     Optional[List[int]] = None							,_a      :     bool = False       ):


     '''simple docstring'''






     if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=_a							,token_ids_a=_a							,already_has_special_tokens=_a       )

     if token_ids_a is None:
        return [1] + ([0] * len(_a       )) + [1]
     return [1] + ([0] * len(_a       )) + [1, 1] + ([0] * len(_a       )) + [1]


  def   __lowercase		(		self      :     Tuple							,_a      :     List[int]							,_a      :     Optional[List[int]] = None       ):


     '''simple docstring'''






     _a				:	List[str]			=   [self.sep_token_id]
     _a				:	List[str]			=   [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]


  @property
  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     return len(self.fairseq_ids_to_tokens       )


  def   __lowercase		(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	List[str]			=   {self.convert_ids_to_tokens(_a       ): i for i in range(self.vocab_size       )}
     vocab.update(self.added_tokens_encoder       )
     return vocab


  def   __lowercase		(		self      :     Tuple							,_a      :     str       ):


     '''simple docstring'''






     return self.sp_model.encode(_a							,out_type=_a       )


  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     Union[str, Any]       ):


     '''simple docstring'''






     if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
     else:
        return self.unk_token_id


  def   __lowercase		(		self      :     Any							,_a      :     int       ):


     '''simple docstring'''






     return self.fairseq_ids_to_tokens[index]


  def   __lowercase		(		self      :     Tuple							,_a      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	str			=   ''.join(_a       ).replace(_a							,' '       ).strip()
     return out_string


  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     str							,_a      :     Optional[str] = None       ):


     '''simple docstring'''






     if not os.path.isdir(_a       ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
        return
     _a				:	int			=   os.path.join(
         _a							,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )
     _a				:	int			=   os.path.join(
         _a							,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_vocab_file']							,)

     if os.path.abspath(self.vocab_file       ) != os.path.abspath(_a       ) and os.path.isfile(self.vocab_file       ):
        copyfile(self.vocab_file							,_a       )
     elif not os.path.isfile(self.vocab_file       ):
        with open(_a							,'wb'       ) as fi:
           _a				:	List[Any]			=   self.sp_model.serialized_model_proto()
           fi.write(_a       )

     if os.path.abspath(self.monolingual_vocab_file       ) != os.path.abspath(
         _a       ) and os.path.isfile(self.monolingual_vocab_file       ):
        copyfile(self.monolingual_vocab_file							,_a       )
     elif not os.path.isfile(self.monolingual_vocab_file       ):
        with open(_a							,'w'							,encoding='utf-8'       ) as fp:
           for token in self.fairseq_tokens_to_ids:
              if token not in self.all_special_tokens:
                 fp.write(F"""{str(_a       )} \n"""       )

     return out_vocab_file, out_monolingual_vocab_file



