
"""simple docstring"""







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__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{"""vocab_file""": """sentencepiece.model"""}

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

lowerCamelCase__           =	{
    """google/rembert""": 256,
}




class 				A__ (			_lowerCamelCase):
		A_		: str            =			VOCAB_FILES_NAMES
		A_		: str            =			PRETRAINED_VOCAB_FILES_MAP
		A_		: List[str]            =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE="[CLS]" ,	_SCREAMING_SNAKE_CASE="[SEP]" ,	_SCREAMING_SNAKE_CASE="[UNK]" ,	_SCREAMING_SNAKE_CASE="[SEP]" ,	_SCREAMING_SNAKE_CASE="[PAD]" ,	_SCREAMING_SNAKE_CASE="[CLS]" ,	_SCREAMING_SNAKE_CASE="[MASK]" ,	**_SCREAMING_SNAKE_CASE ,	):
			super().__init__(
			    do_lower_case=_SCREAMING_SNAKE_CASE ,	remove_space=_SCREAMING_SNAKE_CASE ,	keep_accents=_SCREAMING_SNAKE_CASE ,	bos_token=_SCREAMING_SNAKE_CASE ,	eos_token=_SCREAMING_SNAKE_CASE ,	unk_token=_SCREAMING_SNAKE_CASE ,	sep_token=_SCREAMING_SNAKE_CASE ,	pad_token=_SCREAMING_SNAKE_CASE ,	cls_token=_SCREAMING_SNAKE_CASE ,	mask_token=_SCREAMING_SNAKE_CASE ,	**_SCREAMING_SNAKE_CASE ,	)

			__lowerCAmelCase    :		Union[str, Any]		    =   do_lower_case
			__lowerCAmelCase    :		Optional[int]		    =   remove_space
			__lowerCAmelCase    :		Union[str, Any]		    =   keep_accents
			__lowerCAmelCase    :		List[str]		    =   vocab_file

			__lowerCAmelCase    :		Tuple		    =   spm.SentencePieceProcessor()
			self.sp_model.Load(_SCREAMING_SNAKE_CASE				)

		@property
		def 							__lowerCamelCase							(   self				):
			return len(self.sp_model				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Any		    =   {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE				): i for i in range(self.vocab_size				)}
			vocab.update(self.added_tokens_encoder				)
			return vocab

		def __getstate__(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.__dict__.copy()
			__lowerCAmelCase    :		List[Any]		    =   None
			return state

		def __setstate__(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Dict		    =   d
			__lowerCAmelCase    :		Tuple		    =   spm.SentencePieceProcessor()
			self.sp_model.Load(self.vocab_file				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=False				):
			__lowerCAmelCase    :		Tuple		    =   self.sp_model.EncodeAsPieces(_SCREAMING_SNAKE_CASE				)
			return pieces

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			return self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			return self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE				)

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Dict		    =   self.sp_model.decode_pieces(_SCREAMING_SNAKE_CASE				)
			return out_string

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):
			__lowerCAmelCase    :		Tuple		    =   [self.sep_token_id]
			__lowerCAmelCase    :		Optional[Any]		    =   [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 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None ,	_SCREAMING_SNAKE_CASE = 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(_SCREAMING_SNAKE_CASE				)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1]
			return [1] + ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1]

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):
			__lowerCAmelCase    :		Optional[Any]		    =   [self.sep_token_id]
			__lowerCAmelCase    :		Union[str, 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				) * [0] + len(token_ids_a + sep				) * [1]







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE = None				):
			if not os.path.isdir(_SCREAMING_SNAKE_CASE				):
				logger.error('Vocabulary path ({}) should be a directory'.format(_SCREAMING_SNAKE_CASE				)				)
				return
			__lowerCAmelCase    :		Optional[int]		    =   os.path.join(
			    _SCREAMING_SNAKE_CASE ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)

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

			return (out_vocab_file,)

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase	):
	if n_term == "":
		return []
	__lowerCAmelCase    :		list		    =   []
	for temp in range(int(_UpperCamelCase	)	):
		series.append(F"1/{temp + 1}" if series else '1'	)
	return series


if __name__ == "__main__":
							lowerCamelCase__           =	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))