






import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
   import torch


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


A_		:		Dict		     =			logging.get_logger(__name__)
A_		:		Tuple		     =			{'vocab_file': 'spiece.model'}

A_		:		List[Any]		     =			{
    'vocab_file': {
        'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
    }
}

A_		:		Optional[int]		     =			{
    'AI-Sweden/gpt-sw3-126m': 2048,
    'AI-Sweden/gpt-sw3-350m': 2048,
    'AI-Sweden/gpt-sw3-1.6b': 2048,
    'AI-Sweden/gpt-sw3-6.7b': 2048,
    'AI-Sweden/gpt-sw3-20b': 2048,
}




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		List[Any]									=VOCAB_FILES_NAMES
       a    :		Union[str, Any]									=PRETRAINED_VOCAB_FILES_MAP
       a    :		str									=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a    :		Any									=['''input_ids''', '''attention_mask''']





       def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase=False  ,      _lowerCamelCase=False  ,      _lowerCamelCase=False  ,      _lowerCamelCase=None  ,      _lowerCamelCase=None  ,      _lowerCamelCase=None  ,      _lowerCamelCase=None  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Dict   				=    {} if sp_model_kwargs is None else sp_model_kwargs

         UpperCamelCase_: str   				=    kwargs.get('name_or_path'							)
         if name_or_path is None:
           logger.warning(
               'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
               ' you are testing the model, this can safely be ignored'							)
           UpperCamelCase_: Optional[int]   				=    'None'

         # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
         UpperCamelCase_: Tuple   				=    '<|endoftext|>' if eos_token is None else eos_token
         UpperCamelCase_: str   				=    '<unk>' if unk_token is None else unk_token
         if "gpt-sw3-7b" in name_or_path:
           UpperCamelCase_: List[str]   				=    unk_token if pad_token is None else pad_token
           UpperCamelCase_: Tuple   				=    eos_token if bos_token is None else bos_token
         else:
           UpperCamelCase_: Optional[int]   				=    '<pad>' if pad_token is None else pad_token
           UpperCamelCase_: Dict   				=    '<s>' if bos_token is None else bos_token

         super().__init__(
             do_lower_case=_lowerCamelCase  ,      remove_space=_lowerCamelCase  ,      keep_accents=_lowerCamelCase  ,      bos_token=_lowerCamelCase  ,      eos_token=_lowerCamelCase  ,      unk_token=_lowerCamelCase  ,      pad_token=_lowerCamelCase  ,      sp_model_kwargs=self.sp_model_kwargs  ,      **_lowerCamelCase  ,      )

         UpperCamelCase_: Any   				=    do_lower_case
         UpperCamelCase_: int   				=    remove_space
         UpperCamelCase_: Dict   				=    keep_accents
         UpperCamelCase_: Any   				=    vocab_file

         UpperCamelCase_: List[str]   				=    spm.SentencePieceProcessor(**self.sp_model_kwargs							)
         self.sp_model.Load(_lowerCamelCase							)

         # Used for whitespace normalization in input texts
         # fmt : off
         UpperCamelCase_: List[str]   				=    {' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
         # fmt : on

         # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
         UpperCamelCase_: List[Any]   				=    re.compile(
             f'''[{''.join(map(_lowerCamelCase  ,      list(range(0  ,      9							)							) + list(range(1_1  ,      3_2							)							) + list(range(1_2_7  ,      1_6_0							)							) + [1_6_0, 1_7_3, 8_2_0_3]							)							)}]'''							)





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





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

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

         UpperCamelCase_: Dict   				=    spm.SentencePieceProcessor(**self.sp_model_kwargs							)
         self.sp_model.Load(self.vocab_file							)





       @property
       # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
       def       _a   ( self							):
         return len(self.sp_model							)





       def       _a   ( self  ,      _lowerCamelCase							):
         UpperCamelCase_: Optional[Any]   				=    self.non_printing_characters_re.sub(''  ,      _lowerCamelCase							)

         # Normalize whitespaces
         UpperCamelCase_: List[Any]   				=    ''.join([char if char not in self.whitespaces else ' ' for char in text]							)

         # NFC Unicode normalization
         UpperCamelCase_: Tuple   				=    unicodedata.normalize('NFC'  ,      _lowerCamelCase							)
         return text





       def       _a   ( self  ,      _lowerCamelCase  ,      **_lowerCamelCase							):
         UpperCamelCase_: Tuple   				=    self.preprocess_text(_lowerCamelCase							)
         return self.sp_model.encode(_lowerCamelCase  ,      out_type=_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase							):
         return self.sp_model.PieceToId(_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase							):
         return self.sp_model.IdToPiece(_lowerCamelCase							)





       @staticmethod
       def       _a   ( _lowerCamelCase							):
         return out_string





       def       _a   ( self  ,      _lowerCamelCase							):
         UpperCamelCase_: Tuple   				=    []
         UpperCamelCase_: Tuple   				=    ''
         UpperCamelCase_: Tuple   				=    False
         for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
             # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
             if not prev_is_special:
               out_string += " "

             out_string += self.sp_model.decode(_lowerCamelCase							) + token
             UpperCamelCase_: Union[str, Any]   				=    True
             UpperCamelCase_: Union[str, Any]   				=    []
           else:
             current_sub_tokens.append(_lowerCamelCase							)
             UpperCamelCase_: Optional[int]   				=    False
         out_string += self.sp_model.decode(_lowerCamelCase							)

         return out_string





       def       _a   ( self							):
         UpperCamelCase_: str   				=    {self.convert_ids_to_tokens(_lowerCamelCase							): i for i in range(self.vocab_size							)}
         vocab.update(self.added_tokens_encoder							)
         return vocab





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None							):
         if not os.path.isdir(_lowerCamelCase							):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
           return
         UpperCamelCase_: List[Any]   				=    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:
             UpperCamelCase_: Optional[int]   				=    self.sp_model.serialized_model_proto()
             fi.write(_lowerCamelCase							)

         return (out_vocab_file,)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = False							):

         if isinstance(_lowerCamelCase  ,      _lowerCamelCase							):
           UpperCamelCase_: str   				=    self.preprocess_text(_lowerCamelCase							)
           UpperCamelCase_: str   				=    self.sp_model.encode(_lowerCamelCase							)
         else:
           UpperCamelCase_: List[str]   				=    [self.preprocess_text(_lowerCamelCase							) for t in text]
           UpperCamelCase_: Dict   				=    self.sp_model.encode(_lowerCamelCase							)

         if return_tensors is True or return_tensors == "pt":
           UpperCamelCase_: Optional[int]   				=    torch.tensor(_lowerCamelCase							)

         return token_ids





       def       _a   ( self  ,      _lowerCamelCase							):
         return self.sp_model.decode(_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase							):
         UpperCamelCase_: str   				=    [f'''User: {text}''' if is_user else f'''Bot: {text}''' for is_user, text in conversation.iter_texts()]
         UpperCamelCase_: int   				=    (
             f'''{self.eos_token}{self.bos_token}''' + f'''{self.bos_token}'''.join(_lowerCamelCase							) + f'''{self.bos_token}Bot:'''
         )
         return self.encode(text=_lowerCamelCase							)







def   snake_case (UpperCAmelCase__  )	->    int:
  assert isinstance(UpperCAmelCase__     ,   UpperCAmelCase__  ), F'''The input value of [n={number}] is not an integer'''

  if number == 1:
    return 2
  elif number < 1:
    UpperCamelCase_: List[Any]   				=    F'''The input value of [n={number}] has to be > 0'''
    raise ValueError(UpperCAmelCase__  )
  else:
    UpperCamelCase_: str   				=    sylvester(number - 1  )
    UpperCamelCase_: str   				=    num - 1
    UpperCamelCase_: Any   				=    num
    return lower * upper + 1


if __name__ == "__main__":
   print(F'''The 8th number in Sylvester\'s sequence: {sylvester(8)}''')