





'''simple docstring'''


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

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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				"▁"

_SCREAMING_SNAKE_CASE									=				{"vocab_file": "prophetnet.tokenizer"}

_SCREAMING_SNAKE_CASE									=				{
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

_SCREAMING_SNAKE_CASE									=				{
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

_SCREAMING_SNAKE_CASE									=				{
    "microsoft/xprophetnet-large-wiki100-cased": 5_12,
}


def 						__a(SCREAMING_SNAKE_CASE_	:       Any     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   collections.OrderedDict()
    with open(SCREAMING_SNAKE_CASE_		,							"r"		,							encoding="utf-8"     ) as reader:
        _lowerCAmelCase						      =   reader.readlines()
    for index, token in enumerate(SCREAMING_SNAKE_CASE_     ):
        _lowerCAmelCase						      =   token.rstrip("\n"     )
        _lowerCAmelCase						      =   index
    return vocab





class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Optional[int]								=       VOCAB_FILES_NAMES
 __lowerCamelCase    :				int								=       PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase    :				Optional[Any]								=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase    :				str								=       ["input_ids", "attention_mask"]




 def __init__(						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase="[SEP]"      ,  _lowerCAmelCase="[SEP]"      ,  _lowerCAmelCase="[SEP]"      ,  _lowerCAmelCase="[UNK]"      ,  _lowerCAmelCase="[PAD]"      ,  _lowerCAmelCase="[CLS]"      ,  _lowerCAmelCase="[MASK]"      ,  _lowerCAmelCase = None      ,  **_lowerCAmelCase      ,  )     ->				None:
     _lowerCAmelCase						      =   {} if sp_model_kwargs is None else sp_model_kwargs

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

     try:
         import sentencepiece as spm
     except ImportError:
         logger.warning(
             "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
             " pip install sentencepiece"		)
         raise

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

     # Original fairseq vocab and spm vocab must be "aligned":
     # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
     # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
     # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
     # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

     # put special tokens and [unused] tokens into the vocab
     _lowerCAmelCase						      =   {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

     for i in range(10		):
         _lowerCAmelCase						      =   f'''[unused{i}]'''
         _lowerCAmelCase						      =   5 + i

     # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
     _lowerCAmelCase						      =   12
     _lowerCAmelCase						      =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}
     for k in self.fairseq_tokens_to_ids.keys():
         self.unique_no_split_tokens.append(_lowerCAmelCase		)




 def __getstate__(						self		)     ->				Dict:
     _lowerCAmelCase						      =   self.__dict__.copy()
     _lowerCAmelCase						      =   None
     return state




 def __setstate__(						self      ,  _lowerCAmelCase		)     ->				str:
     _lowerCAmelCase						      =   d
     try:
         import sentencepiece as spm
     except ImportError:
         logger.warning(
             "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
             " pip install sentencepiece"		)
         raise

     # for backward compatibility
     if not hasattr(self      ,  "sp_model_kwargs"		):
         _lowerCAmelCase						      =   {}

     _lowerCAmelCase						      =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
     self.sp_model.Load(self.vocab_file		)




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None      ,  _lowerCAmelCase = False		)     ->				List[int]:

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

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




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None		)     ->				List[int]:
     _lowerCAmelCase						      =   [self.sep_token_id]

     if token_ids_a is None:
         return len(token_ids_a + sep		) * [0]
     return len(token_ids_a + sep + sep + token_ids_a + sep		) * [0]




 @property
 def 				_snake_case (						self		)     ->				List[str]:
     return len(self.sp_model		) + self.fairseq_offset




 def 				_snake_case (						self		)     ->				int:
     _lowerCAmelCase						      =   {self.convert_ids_to_tokens(_lowerCAmelCase		): i for i in range(self.vocab_size		)}
     vocab.update(self.added_tokens_encoder		)
     return vocab




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				str:
     return self.sp_model.encode(_lowerCAmelCase      ,  out_type=_lowerCAmelCase		)




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Optional[int]:
     if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
     _lowerCAmelCase						      =   self.sp_model.PieceToId(_lowerCAmelCase		)

     # Need to return unknown token if the SP model returned 0
     return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Optional[int]:
     if index in self.fairseq_ids_to_tokens:
         return self.fairseq_ids_to_tokens[index]
     return self.sp_model.IdToPiece(index - self.fairseq_offset		)




 def 				_snake_case (						self      ,  _lowerCAmelCase		)     ->				Any:
     _lowerCAmelCase						      =   "".join(_lowerCAmelCase		).replace(_lowerCAmelCase      ,  " "		).strip()
     return out_string




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

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

     return (out_vocab_file,)





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None		)     ->				List[int]:

     if token_ids_a is None:
         return token_ids_a + [self.sep_token_id]
     _lowerCAmelCase						      =   [self.sep_token_id]
     return token_ids_a + sep + token_ids_a + sep









'''simple docstring'''


from __future__ import annotations


def 						__a(SCREAMING_SNAKE_CASE_	:       int | float | str		,							SCREAMING_SNAKE_CASE_	:       int | float | str     ):



    '''simple docstring'''





    if nth_term == "":
        return [""]
    _lowerCAmelCase						      =   int(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   int(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   []
    for temp in range(int(SCREAMING_SNAKE_CASE_     )     ):
        series.append(F'''1 / {pow(temp + 1		,							int(SCREAMING_SNAKE_CASE_     )     )}''' if series else "1"     )
    return series


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       _SCREAMING_SNAKE_CASE									=				int(input("Enter the last number (nth term) of the P-Series"))
       _SCREAMING_SNAKE_CASE									=				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))


