






'''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


_A     :      str   					=     logging.get_logger(__name__)

_A     :      Dict   					=     {'''vocab_file''': '''sentencepiece.bpe.model'''}

_A     :      Optional[Any]   					=     {
    '''vocab_file''': {
        '''moussaKam/mbarthez''': '''https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez''': '''https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model''',
        '''moussaKam/barthez-orangesum-title''': (
            '''https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'''
        ),
    },
}

_A     :      int   					=     {
    '''moussaKam/mbarthez''': 1_024,
    '''moussaKam/barthez''': 1_024,
    '''moussaKam/barthez-orangesum-title''': 1_024,
}

_A     :      Optional[Any]   					=     '''▁'''

class _lowercase     (    _lowercase		):
							a    				=	VOCAB_FILES_NAMES
							a    				=	PRETRAINED_VOCAB_FILES_MAP
							a    				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a    				=	["""input_ids""", """attention_mask"""]




							def __init__(							self:    str      ,							UpperCamelCase__:    Any      ,							UpperCamelCase__:    Optional[int]="<s>"      ,							UpperCamelCase__:    str="</s>"      ,							UpperCamelCase__:    Tuple="</s>"      ,							UpperCamelCase__:    int="<s>"      ,							UpperCamelCase__:    Optional[int]="<unk>"      ,							UpperCamelCase__:    Optional[int]="<pad>"      ,							UpperCamelCase__:    Tuple="<mask>"      ,							UpperCamelCase__:    Optional[Dict[str, Any]] = None      ,							**UpperCamelCase__:    Optional[int]      ,							):
														# Mask token behave like a normal word, i.e. include the space before it
														lowerCamelCase__ :     Dict	  =	AddedToken(UpperCamelCase__      ,							lstrip=UpperCamelCase__      ,							rstrip=UpperCamelCase__					) if isinstance(UpperCamelCase__      ,							UpperCamelCase__					) else mask_token

														lowerCamelCase__ :     Tuple	  =	{} if sp_model_kwargs is None else sp_model_kwargs

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

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

														lowerCamelCase__ :     Union[str, Any]	  =	{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

														lowerCamelCase__ :     Any	  =	len(self.sp_model					) - 1
														lowerCamelCase__ :     int	  =	{v: k for k, v in self.fairseq_tokens_to_ids.items()}




							def 		lowerCamelCase_    (							self:    Optional[int]      ,							UpperCamelCase__:    List[int]      ,							UpperCamelCase__:    Optional[List[int]] = None					):

														if token_ids_a is None:
																					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
														lowerCamelCase__ :     Optional[Any]	  =	[self.cls_token_id]
														lowerCamelCase__ :     Optional[Any]	  =	[self.sep_token_id]
														return cls + token_ids_a + sep + sep + token_ids_a + sep




							def 		lowerCamelCase_    (							self:    List[Any]      ,							UpperCamelCase__:    List[int]      ,							UpperCamelCase__:    Optional[List[int]] = None      ,							UpperCamelCase__:    bool = False					):
														if already_has_special_tokens:
																					return super().get_special_tokens_mask(
																					    token_ids_a=UpperCamelCase__      ,							token_ids_a=UpperCamelCase__      ,							already_has_special_tokens=UpperCamelCase__					)

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




							def 		lowerCamelCase_    (							self:    Any      ,							UpperCamelCase__:    List[int]      ,							UpperCamelCase__:    Optional[List[int]] = None					):
														lowerCamelCase__ :     Dict	  =	[self.sep_token_id]
														lowerCamelCase__ :     Any	  =	[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 		lowerCamelCase_    (							self:    Union[str, Any]					):
														return len(self.sp_model					)




							def 		lowerCamelCase_    (							self:    Tuple					):
														lowerCamelCase__ :     Optional[Any]	  =	{self.convert_ids_to_tokens(UpperCamelCase__					): i for i in range(self.vocab_size					)}
														vocab.update(self.added_tokens_encoder					)
														return vocab




							def 		lowerCamelCase_    (							self:    Optional[Any]      ,							UpperCamelCase__:    str					):
														return self.sp_model.encode(UpperCamelCase__      ,							out_type=UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    List[Any]      ,							UpperCamelCase__:    Optional[int]					):
														if token in self.fairseq_tokens_to_ids:
																					return self.fairseq_tokens_to_ids[token]
														lowerCamelCase__ :     Optional[Any]	  =	self.sp_model.PieceToId(UpperCamelCase__					)

														return spm_id if spm_id else self.unk_token_id




							def 		lowerCamelCase_    (							self:    Tuple      ,							UpperCamelCase__:    int					):
														if index in self.fairseq_ids_to_tokens:
																					return self.fairseq_ids_to_tokens[index]
														return self.sp_model.IdToPiece(UpperCamelCase__					)




							def 		lowerCamelCase_    (							self:    int      ,							UpperCamelCase__:    List[str]					):
														lowerCamelCase__ :     str	  =	[]
														lowerCamelCase__ :     List[Any]	  =	""""""
														lowerCamelCase__ :     int	  =	False
														for token in tokens:
																					# make sure that special tokens are not decoded using sentencepiece model
																					if token in self.all_special_tokens:
																												if not prev_is_special:
																																			out_string += " "
																												out_string += self.sp_model.decode(UpperCamelCase__					) + token
																												lowerCamelCase__ :     int	  =	True
																												lowerCamelCase__ :     List[str]	  =	[]
																					else:
																												current_sub_tokens.append(UpperCamelCase__					)
																												lowerCamelCase__ :     Optional[int]	  =	False
														out_string += self.sp_model.decode(UpperCamelCase__					)
														return out_string.strip()




							def __getstate__(							self:    Union[str, Any]					):
														lowerCamelCase__ :     List[Any]	  =	self.__dict__.copy()
														lowerCamelCase__ :     List[str]	  =	None
														return state




							def __setstate__(							self:    Union[str, Any]      ,							UpperCamelCase__:    List[Any]					):
														lowerCamelCase__ :     Optional[int]	  =	d

														# for backward compatibility
														if not hasattr(self      ,							"""sp_model_kwargs"""					):
																					lowerCamelCase__ :     Union[str, Any]	  =	{}

														lowerCamelCase__ :     str	  =	spm.SentencePieceProcessor(**self.sp_model_kwargs					)
														self.sp_model.Load(self.vocab_file					)




							def 		lowerCamelCase_    (							self:    Any      ,							UpperCamelCase__:    str      ,							UpperCamelCase__:    Optional[str] = None					):
														if not os.path.isdir(UpperCamelCase__					):
																					logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
																					return
														lowerCamelCase__ :     int	  =	os.path.join(
														    UpperCamelCase__      ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

														if os.path.abspath(self.vocab_file					) != os.path.abspath(UpperCamelCase__					) and os.path.isfile(self.vocab_file					):
																					copyfile(self.vocab_file      ,							UpperCamelCase__					)
														elif not os.path.isfile(self.vocab_file					):
																					with open(UpperCamelCase__      ,							"""wb"""					) as fi:
																												lowerCamelCase__ :     str	  =	self.sp_model.serialized_model_proto()
																												fi.write(UpperCamelCase__					)

														return (out_vocab_file,)









'''simple docstring'''







from __future__ import annotations





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	list[str]:
							if nth_term == "":
														return [""]
							lowerCamelCase__ :     str	  =	int(UpperCamelCase				)
							lowerCamelCase__ :     Union[str, Any]	  =	int(UpperCamelCase				)
							lowerCamelCase__ :     list[str]	  =	[]
							for temp in range(int(UpperCamelCase				)				):
														series.append(f'''1 / {pow(temp + 1    ,      int(UpperCamelCase				)				)}''' if series else """1"""				)
							return series


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			_A     :      Optional[Any]   					=int(input('''Enter the last number (nth term) of the P-Series'''))
			_A     :      List[str]   					=int(input('''Enter the power for  P-Series'''))
			print('''Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p''')
			print(p_series(nth_term, power))

