



"""simple docstring"""

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

import sentencepiece as spm

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


lowercase__					:						Dict												=			logging.get_logger(__name__)


lowercase__					:						List[str]												=			'''▁'''

lowercase__					:						Optional[Any]												=			{'''vocab_file''': '''spiece.model'''}

lowercase__					:						List[str]												=			{
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    }
}

lowercase__					:						List[Any]												=			{
    '''google/reformer-crime-and-punishment''': 52_42_88,
}




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Optional[Any]	      =	VOCAB_FILES_NAMES
 _lowerCAmelCase     :    List[Any]	      =	PRETRAINED_VOCAB_FILES_MAP
 _lowerCAmelCase     :    int	      =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCAmelCase     :    str	      =	["""input_ids""", """attention_mask"""]

 def __init__(  self	: Optional[int]						,  lowercase_	: int						,  lowercase_	: Union[str, Any]="</s>"						,  lowercase_	: Any="<unk>"						,  lowercase_	: Tuple=[]						,  lowercase_	: Optional[Dict[str, Any]] = None						,  **lowercase_	: int						,  ):
     snake_case_				:  Union[str, Any]       =			{} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         eos_token=lowercase_						,  unk_token=lowercase_						,  additional_special_tokens=lowercase_						,  sp_model_kwargs=self.sp_model_kwargs						,  **lowercase_						,  )

     snake_case_				:  List[Any]       =			vocab_file
     snake_case_				:  Union[str, Any]       =			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
     self.sp_model.Load(lowercase_       )

 @property
 def 		_snake_case      (  self	: List[Any]       ):
     return self.sp_model.get_piece_size()

 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_				:  Tuple       =			{self.convert_ids_to_tokens(lowercase_       ): i for i in range(self.vocab_size       )}
     vocab.update(self.added_tokens_encoder       )
     return vocab

 def __getstate__(  self	: Dict       ):
     snake_case_				:  Union[str, Any]       =			self.__dict__.copy()
     snake_case_				:  Optional[Any]       =			None
     return state

 def __setstate__(  self	: Union[str, Any]						,  lowercase_	: List[Any]       ):
     snake_case_				:  Tuple       =			d

     # for backward compatibility
     if not hasattr(self						,  '''sp_model_kwargs'''       ):
         snake_case_				:  int       =			{}

     snake_case_				:  List[str]       =			spm.SentencePieceProcessor(**self.sp_model_kwargs       )
     self.sp_model.Load(self.vocab_file       )

 def 		_snake_case      (  self	: str						,  lowercase_	: str       ):
     return self.sp_model.encode(lowercase_						,  out_type=lowercase_       )

 def 		_snake_case      (  self	: Dict						,  lowercase_	: Optional[Any]       ):
     return self.sp_model.piece_to_id(lowercase_       )

 def 		_snake_case      (  self	: Optional[Any]						,  lowercase_	: str       ):
     if index < self.sp_model.get_piece_size():
         snake_case_				:  Optional[Any]       =			self.sp_model.IdToPiece(lowercase_       )
     return token

 def 		_snake_case      (  self	: Dict						,  lowercase_	: Optional[Any]       ):
     snake_case_				:  Tuple       =			[]
     snake_case_				:  Optional[Any]       =			''''''
     for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
             out_string += self.sp_model.decode(lowercase_       ) + token
             snake_case_				:  Union[str, Any]       =			[]
         else:
             current_sub_tokens.append(lowercase_       )
     out_string += self.sp_model.decode(lowercase_       )
     return out_string.strip()






 def 		_snake_case      (  self	: Dict						,  lowercase_	: str						,  lowercase_	: Optional[str] = None       ):
     if not os.path.isdir(lowercase_       ):
         logger.error(f"Vocabulary path ({save_directory}) should be a directory"       )
         return
     snake_case_				:  Tuple       =			os.path.join(
         lowercase_						,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )

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

     return (out_vocab_file,)










"""simple docstring"""

def __lowercase    (							_a	,    _a					):
    snake_case_				:  str       =			[0 for i in range(r + 1					)]
    # nc0 = 1
    snake_case_				:  int       =			1
    for i in range(1	,    n + 1					):
        # to compute current row from previous row.
        snake_case_				:  Any       =			min(_a	,    _a					)
        while j > 0:
            c[j] += c[j - 1]
            j -= 1
    return c[r]






print(binomial_coefficient(n=10, r=5))





