


'''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


_A			  =  logging.get_logger(__name__)


_A			  =  """▁"""

_A			  =  {"""vocab_file""": """spiece.model"""}

_A			  =  {
    """vocab_file""": {
        """google/reformer-crime-and-punishment""": (
            """https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"""
        )
    }
}

_A			  =  {
    """google/reformer-crime-and-punishment""": 52_42_88,
}






class   SCREAMING_SNAKE_CASE_						(			lowerCamelCase__		):
			__a       :   List[Any]       =	VOCAB_FILES_NAMES
			__a       :   List[str]       =	PRETRAINED_VOCAB_FILES_MAP
			__a       :   Optional[int]       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__a       :   str       =	["""input_ids""", """attention_mask"""]




			def __init__(   self       ,   lowercase       ,   lowercase="</s>"       ,   lowercase="<unk>"       ,   lowercase=[]       ,   lowercase = None       ,   **lowercase       ,   )      ->    None:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE       :				int       =			{} if sp_model_kwargs is None else sp_model_kwargs

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

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




			@property
			def       _snake_case (   self							)      ->    List[str]:


								'''simple docstring'''






								return self.sp_model.get_piece_size()




			def       _snake_case (   self							)      ->    Dict[str, int]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE       :				Tuple       =			{self.convert_ids_to_tokens(snake_case__							): i for i in range(self.vocab_size							)}
								vocab.update(self.added_tokens_encoder							)
								return vocab




			def __getstate__(   self							)      ->    Tuple:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE       :				Optional[int]       =			self.__dict__.copy()
								__SCREAMING_SNAKE_CASE       :				Union[str, Any]       =			None
								return state




			def __setstate__(   self       ,   lowercase							)      ->    Dict:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE       :				int       =			d

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

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




			def       _snake_case (   self       ,   lowercase							)      ->    List[str]:


								'''simple docstring'''






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




			def       _snake_case (   self       ,   lowercase							)      ->    List[Any]:


								'''simple docstring'''






								return self.sp_model.piece_to_id(snake_case__							)




			def       _snake_case (   self       ,   lowercase							)      ->    int:


								'''simple docstring'''






								if index < self.sp_model.get_piece_size():
													__SCREAMING_SNAKE_CASE       :				Union[str, Any]       =			self.sp_model.IdToPiece(snake_case__							)
								return token




			def       _snake_case (   self       ,   lowercase							)      ->    Dict:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE       :				Optional[int]       =			[]
								__SCREAMING_SNAKE_CASE       :				List[str]       =			""""""
								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(snake_case__							) + token
																		__SCREAMING_SNAKE_CASE       :				int       =			[]
													else:
																		current_sub_tokens.append(snake_case__							)
								out_string += self.sp_model.decode(snake_case__							)
								return out_string.strip()




			def       _snake_case (   self       ,   lowercase       ,   lowercase = None							)      ->    Tuple[str]:


								'''simple docstring'''






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

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

								return (out_vocab_file,)









def  __magic_name__  (				lowercase				)				-> list:

							"""simple docstring"""



							if n_term == "":
														return []
							lowercase_	:   list  				=     []
							for temp in range(int(lowercase				)				):
														series.append(f"""1/{temp + 1}""" if series else """1"""				)
							return series


if __name__ == "__main__":
							UpperCAmelCase_							=				input("""Enter the last number (nth term) of the Harmonic Series""")
							print("""Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n""")
							print(harmonic_series(nth_term))