


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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__							:						int							     =   logging.get_logger(__name__)

lowerCamelCase__							:						List[Any]							     =   {"""vocab_file""": """sentencepiece.model"""}

lowerCamelCase__							:						Dict							     =   {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

lowerCamelCase__							:						List[str]							     =   {
    """google/rembert""": 2_5_6,
}
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							List[Any]	   =       VOCAB_FILES_NAMES
     __lowercase				:							Tuple	   =       PRETRAINED_VOCAB_FILES_MAP
     __lowercase				:							Union[str, Any]	   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

     def __init__(					self:Union[str, Any]     ,		_a:Union[str, Any]     ,		_a:Tuple=False     ,		_a:Dict=True     ,		_a:int=True     ,		_a:Optional[Any]="[CLS]"     ,		_a:int="[SEP]"     ,		_a:Dict="[UNK]"     ,		_a:Optional[int]="[SEP]"     ,		_a:Any="[PAD]"     ,		_a:Union[str, Any]="[CLS]"     ,		_a:Dict="[MASK]"     ,		**_a:str     ,		):
       super().__init__(
           do_lower_case=_a     ,		remove_space=_a     ,		keep_accents=_a     ,		bos_token=_a     ,		eos_token=_a     ,		unk_token=_a     ,		sep_token=_a     ,		pad_token=_a     ,		cls_token=_a     ,		mask_token=_a     ,		**_a     ,		)

       snake_case__						= do_lower_case
       snake_case__						= remove_space
       snake_case__						= keep_accents
       snake_case__						= vocab_file

       snake_case__						= spm.SentencePieceProcessor()
       self.sp_model.Load(_a   )

     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       return len(self.sp_model   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       snake_case__						= {self.convert_ids_to_tokens(_a   ): i for i in range(self.vocab_size   )}
       vocab.update(self.added_tokens_encoder   )
       return vocab

     def __getstate__(					self:str   ):
       snake_case__						= self.__dict__.copy()
       snake_case__						= None
       return state

     def __setstate__(					self:List[Any]     ,		_a:Optional[int]   ):
       snake_case__						= d
       snake_case__						= spm.SentencePieceProcessor()
       self.sp_model.Load(self.vocab_file   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]     ,		_a:Tuple     ,		_a:Any=False   ):
       snake_case__						= self.sp_model.EncodeAsPieces(_a   )
       return pieces

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:Optional[int]   ):
       return self.sp_model.PieceToId(_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]     ,		_a:Optional[int]   ):
       return self.sp_model.IdToPiece(_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]     ,		_a:Union[str, Any]   ):
       snake_case__						= self.sp_model.decode_pieces(_a   )
       return out_string

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:List[int]     ,		_a:Optional[List[int]] = None   ):
       snake_case__						= [self.sep_token_id]
       snake_case__						= [self.cls_token_id]
       if token_ids_a is None:
         return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep

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

       if already_has_special_tokens:
         if token_ids_a is not None:
           raise ValueError(
               '''You should not supply a second sequence if the provided sequence of '''
               '''ids is already formatted with special tokens for the model.'''   )
         return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

       if token_ids_a is not None:
         return [1] + ([0] * len(_a   )) + [1] + ([0] * len(_a   )) + [1]
       return [1] + ([0] * len(_a   )) + [1]

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]     ,		_a:List[int]     ,		_a:Optional[List[int]] = None   ):
       snake_case__						= [self.sep_token_id]
       snake_case__						= [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   ) * [0] + len(token_ids_a + sep   ) * [1]







     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		_a:str     ,		_a:Optional[str] = None   ):
       if not os.path.isdir(_a   ):
         logger.error('''Vocabulary path ({}) should be a directory'''.format(_a   )   )
         return
       snake_case__						= os.path.join(
           _a     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

       if os.path.abspath(self.vocab_file   ) != os.path.abspath(_a   ):
         copyfile(self.vocab_file     ,		_a   )

       return (out_vocab_file,)







'''simple docstring'''


from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
lowerCAmelCase_      :  List[Any]				     =  2_9979_2458

# Symbols
lowerCAmelCase_     ,   lowerCAmelCase_     ,   lowerCAmelCase_     ,   lowerCAmelCase_      :  Tuple				     =  symbols('ct x y z')



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float				):




     """simple docstring"""


     if velocity > c:
          raise ValueError("""Speed must not exceed light speed 299,792,458 [m/s]!"""				)
     elif velocity < 1:
          # Usually the speed should be much higher than 1 (c order of magnitude)
          raise ValueError("""Speed must be greater than or equal to 1!"""				)

     return velocity / c



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float				):




     """simple docstring"""


     return 1 / sqrt(1 - beta(UpperCamelCase__				) ** 2				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float				):




     """simple docstring"""


     return np.array(
         [
             [gamma(UpperCamelCase__				), -gamma(UpperCamelCase__				) * beta(UpperCamelCase__				), 0, 0],
             [-gamma(UpperCamelCase__				) * beta(UpperCamelCase__				), gamma(UpperCamelCase__				), 0, 0],
             [0, 0, 1, 0],
             [0, 0, 0, 1],
         ]				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					float ,    UpperCamelCase__    :					np.ndarray | None = None				):




     """simple docstring"""


     if event is None:
          a_     :							str     						=  np.array([ct, x, y, z]				)  # Symbolic four vector
     else:
          event[0] *= c  # x0 is ct (speed of light * time)

     return transformation_matrix(UpperCamelCase__				) @ event


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 # Example of symbolic vector:
 lowerCAmelCase_      :  Union[str, Any]				     =  transform(2997_9245)
 print('Example of four vector: ')
 print(f"ct' = {four_vector[0]}")
 print(f"x' = {four_vector[1]}")
 print(f"y' = {four_vector[2]}")
 print(f"z' = {four_vector[3]}")

 # Substitute symbols with numerical values
 lowerCAmelCase_      :  Tuple				     =  {ct: c, x: 1, y: 1, z: 1}
 lowerCAmelCase_      :  Optional[int]				     =  [four_vector[i].subs(sub_dict) for i in range(4)]

 print(f"\n{numerical_vector}")
