


import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


snake_case    :   Optional[int]	    =					logging.get_logger(__name__)

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

snake_case    :   str	    =					{'''vocab_file''': '''vocab.txt''', '''sentencepiece_model_ckpt''': '''sentencepiece.bpe.model'''}

snake_case    :   Union[str, Any]	    =					{
    '''sentencepiece_model_file''': '''sentencepiece.bpe.model''',
    '''vocab_file''': '''vocab.txt''',
}

snake_case    :   Optional[int]	    =					{
    '''vocab_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
    },
    '''sentencepiece_model_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
    },
}

snake_case    :   Optional[Any]	    =					{
    '''ernie-m-base''': 5_14,
    '''ernie-m-large''': 5_14,
}

snake_case    :   List[Any]	    =					{
    '''ernie-m-base''': {'''do_lower_case''': False},
    '''ernie-m-large''': {'''do_lower_case''': False},
}







class 		_snake_case  (				_snake_case							):
 SCREAMING_SNAKE_CASE__				=	["input_ids"]

 SCREAMING_SNAKE_CASE__				=	VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE__				=	PRETRAINED_INIT_CONFIGURATION
 SCREAMING_SNAKE_CASE__				=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE__				=	PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE__				=	RESOURCE_FILES_NAMES




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase=None ,  _lowerCamelCase=False ,  _lowerCamelCase="utf8" ,  _lowerCamelCase="[UNK]" ,  _lowerCamelCase="[SEP]" ,  _lowerCamelCase="[PAD]" ,  _lowerCamelCase="[CLS]" ,  _lowerCamelCase="[MASK]" ,  _lowerCamelCase = None ,  **_lowerCamelCase ,  ):
       # Mask token behave like a normal word, i.e. include the space before it and
       # is included in the raw text, there should be a match in a non-normalized sentence.

       a						:Tuple										=	{} if sp_model_kwargs is None else sp_model_kwargs
       super().__init__(
           do_lower_case=_lowerCamelCase ,  unk_token=_lowerCamelCase ,  sep_token=_lowerCamelCase ,  pad_token=_lowerCamelCase ,  cls_token=_lowerCamelCase ,  mask_token=_lowerCamelCase ,  vocab_file=_lowerCamelCase ,  encoding=_lowerCamelCase ,  sp_model_kwargs=self.sp_model_kwargs ,  **_lowerCamelCase ,  )
       a						:str										=	do_lower_case
       a						:str										=	sentencepiece_model_ckpt
       a						:Union[str, Any]										=	spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(_lowerCamelCase   )

       # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
       if vocab_file is not None:
             a						:int										=	self.load_vocab(filepath=_lowerCamelCase   )
       else:
             a						:Optional[Any]										=	{self.sp_model.id_to_piece(_lowerCamelCase   ): id for id in range(self.sp_model.get_piece_size()   )}
       a						:List[Any]										=	{v: k for k, v in self.vocab.items()}




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       if text is None:
             return None

       a						:Dict										=	self.tokenize(_lowerCamelCase   )
       a							,    a						:Optional[int]										=	'''''', []

       for i, ch in enumerate(_lowerCamelCase   ):
             if ch in self.SP_CHAR_MAPPING:
                   a						:Tuple										=	self.SP_CHAR_MAPPING.get(_lowerCamelCase   )
             else:
                   a						:Any										=	unicodedata.normalize('''NFKC''' ,  _lowerCamelCase   )
             if self.is_whitespace(_lowerCamelCase   ):
                   continue
             normalized_text += ch
             char_mapping.extend([i] * len(_lowerCamelCase   )   )

       a							,    a							,    a						:List[str]										=	normalized_text, [], 0

       if self.do_lower_case:
             a						:int										=	text.lower()

       for token in split_tokens:
             if token[:1] == "▁":
                   a						:Optional[Any]										=	token[1:]
             a						:List[str]										=	text[offset:].index(_lowerCamelCase   ) + offset
             a						:Optional[Any]										=	start + len(_lowerCamelCase   )

             token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)   )
             a						:Any										=	end
       return token_mapping




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return len(self.vocab   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return dict(self.vocab ,  **self.added_tokens_encoder   )




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




 def __setstate__(		self ,  _lowerCamelCase   ):
       a						:str										=	d

       # for backward compatibility
       if not hasattr(self ,  '''sp_model_kwargs'''   ):
             a						:Optional[Any]										=	{}

       a						:Any										=	spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(self.sentencepiece_model_ckpt   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       return "".join((self.SP_CHAR_MAPPING.get(_lowerCamelCase ,  _lowerCamelCase   ) for c in text)   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=False ,  _lowerCamelCase=64 ,  _lowerCamelCase=0.1   ):

       if self.sp_model_kwargs.get('''enable_sampling'''   ) is True:
             a						:Dict										=	True
       if self.sp_model_kwargs.get('''alpha'''   ) is not None:
             a						:List[str]										=	self.sp_model_kwargs.get('''alpha'''   )
       if self.sp_model_kwargs.get('''nbest_size'''   ) is not None:
             a						:Any										=	self.sp_model_kwargs.get('''nbest_size'''   )

       if not enable_sampling:
             a						:Optional[int]										=	self.sp_model.EncodeAsPieces(_lowerCamelCase   )
       else:
             a						:Optional[int]										=	self.sp_model.SampleEncodeAsPieces(_lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   )
       a						:List[str]										=	[]
       for pi, piece in enumerate(_lowerCamelCase   ):
             if piece == SPIECE_UNDERLINE:
                   if not pieces[pi + 1].startswith(_lowerCamelCase   ) and pi != 0:
                         new_pieces.append(_lowerCamelCase   )
                         continue
                   else:
                         continue
             a						:Optional[int]										=	0
             for i, chunk in enumerate(_lowerCamelCase   ):
                   if chunk == SPIECE_UNDERLINE:
                         continue
                   if self.is_ch_char(_lowerCamelCase   ) or self.is_punct(_lowerCamelCase   ):
                         if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                               new_pieces.append(piece[lst_i:i]   )
                         new_pieces.append(_lowerCamelCase   )
                         a						:Optional[int]										=	i + 1
                   elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
                         if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                               new_pieces.append(piece[lst_i:i]   )
                         a						:int										=	i
                   elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
                         if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                               new_pieces.append(piece[lst_i:i]   )
                         a						:List[str]										=	i
             if len(_lowerCamelCase   ) > lst_i:
                   new_pieces.append(piece[lst_i:]   )
       return new_pieces




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:Optional[Any]										=	''''''.join(_lowerCamelCase   ).replace(_lowerCamelCase ,  ''' '''   ).strip()
       return out_string




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:Dict										=	self.convert_ids_to_tokens(_lowerCamelCase   )
       a						:Optional[int]										=	''''''.join(_lowerCamelCase   ).replace(_lowerCamelCase ,  ''' '''   ).strip()
       return out_string




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       return self.vocab.get(_lowerCamelCase ,  self.vocab.get(self.unk_token   )   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       return self.reverse_vocab.get(_lowerCamelCase ,  self.unk_token   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=None   ):
       if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       a						:Dict										=	[self.cls_token_id]
       a						:Dict										=	[self.sep_token_id]
       return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=None   ):
       if offset_mapping_a is None:
             return [(0, 0)] + offset_mapping_a + [(0, 0)]

       return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=None ,  _lowerCamelCase=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(_lowerCamelCase   )) + [1, 1] + ([0] * len(_lowerCamelCase   )) + [1]
       return [1] + ([0] * len(_lowerCamelCase   )) + [1]




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None   ):
       # called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
       if token_ids_a is None:
             # [CLS] X [SEP]
             return (len(_lowerCamelCase   ) + 2) * [0]

       # [CLS] A [SEP] [SEP] B [SEP]
       return [0] * (len(_lowerCamelCase   ) + 1) + [1] * (len(_lowerCamelCase   ) + 3)




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       if "\u4e00" <= char <= "\u9fff":
             return True
       return False




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       if ("a" <= char <= "z") or ("A" <= char <= "Z"):
             return True
       return False




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       if char in ",;:.?!~，；：。？！《》【】":
             return True
       return False




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       if char == " " or char == "\t" or char == "\n" or char == "\r":
             return True
       if len(_lowerCamelCase   ) == 1:
             a						:Union[str, Any]										=	unicodedata.category(_lowerCamelCase   )
             if cat == "Zs":
                   return True
       return False




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
       a						:Dict										=	{}
       with io.open(_lowerCamelCase ,  '''r''' ,  encoding='''utf-8'''   ) as f:
             for index, line in enumerate(_lowerCamelCase   ):
                   a						:Tuple										=	line.rstrip('''\n'''   )
                   a						:int										=	int(_lowerCamelCase   )

       return token_to_idx




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase = None   ):
       a						:List[Any]										=	0
       if os.path.isdir(_lowerCamelCase   ):
             a						:List[Any]										=	os.path.join(
                 _lowerCamelCase ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
       else:
             a						:str										=	(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
       with open(_lowerCamelCase ,  '''w''' ,  encoding='''utf-8'''   ) as writer:
             for token, token_index in sorted(self.vocab.items() ,  key=lambda _lowerCamelCase   : kv[1]   ):
                   if index != token_index:
                         logger.warning(
                             F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
                             ''' Please check that the vocabulary is not corrupted!'''   )
                         a						:int										=	token_index
                   writer.write(token + '''\n'''   )
                   index += 1

       a						:Any										=	os.path.join(_lowerCamelCase ,  '''sentencepiece.bpe.model'''   )
       with open(_lowerCamelCase ,  '''wb'''   ) as fi:
             a						:Optional[Any]										=	self.sp_model.serialized_model_proto()
             fi.write(_lowerCamelCase   )

       return (vocab_file,)









def     __lowerCamelCase						(			UpperCAmelCase_						:		str						):




      """simple docstring"""





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


if __name__ == "__main__":
     snake_case    :   Tuple	    =					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))





