






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


__lowerCamelCase	:		int										=   """▁"""

__lowerCamelCase	:		str										=   {"""vocab_file""": """spiece.model"""}

__lowerCamelCase	:		List[str]										=   {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""}
}

__lowerCamelCase	:		Tuple										=   {
    """google/pegasus-xsum""": 5_12,
}


__lowerCamelCase	:		Optional[Any]										=   logging.get_logger(__name__)







class  _lowercase   (   __UpperCAmelCase						):
      _a							:						List[Any]										=							VOCAB_FILES_NAMES

      _a							:						List[str]										=							VOCAB_FILES_NAMES
      _a							:						int										=							PRETRAINED_VOCAB_FILES_MAP
      _a							:						List[Any]										=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _a							:						List[Any]										=							["input_ids", "attention_mask"]
      def __init__(     self				,			a				,			a="<pad>"				,			a="</s>"				,			a="<unk>"				,			a="<mask_2>"				,			a="<mask_1>"				,			a=None				,			a=1_0_3				,			a = None				,			**a				,			):
       snake_case__  :    int							       =offset
       if additional_special_tokens is not None:
        if not isinstance(_lowerCamelCase				,			_lowerCamelCase   ):
         raise TypeError(
             F"additional_special_tokens should be of type {type(_lowerCamelCase   )}, but is"
             F" {type(_lowerCamelCase   )}"   )

        snake_case__  :    Union[str, Any]							       =(
            ([mask_token_sent] + additional_special_tokens)
            if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
            else additional_special_tokens
        )
        # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
        additional_special_tokens_extended += [
            F"<unk_{i}>" for i in range(len(_lowerCamelCase   )				,			self.offset - 1   )
        ]

        if len(set(_lowerCamelCase   )   ) != len(_lowerCamelCase   ):
         raise ValueError(
             """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
             F" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."   )
        snake_case__  :    Optional[Any]							       =additional_special_tokens_extended
       else:
        snake_case__  :    Optional[int]							       =[mask_token_sent] if mask_token_sent is not None else []
        additional_special_tokens += [F"<unk_{i}>" for i in range(2				,			self.offset   )]

       snake_case__  :    Tuple							       ={} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           eos_token=_lowerCamelCase				,			unk_token=_lowerCamelCase				,			mask_token=_lowerCamelCase				,			pad_token=_lowerCamelCase				,			mask_token_sent=_lowerCamelCase				,			offset=_lowerCamelCase				,			additional_special_tokens=_lowerCamelCase				,			sp_model_kwargs=self.sp_model_kwargs				,			**_lowerCamelCase				,			)
       snake_case__  :    Union[str, Any]							       =mask_token_sent
       snake_case__  :    Tuple							       =vocab_file
       snake_case__  :    List[Any]							       =spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(_lowerCamelCase   )

       # add special tokens to encoder dict
       snake_case__  :    Optional[int]							       ={
           0: self.pad_token,
           1: self.eos_token,
       }

       if self.mask_token_sent is not None:
        self.encoder.update(
            {
                2: self.mask_token_sent,
                3: self.mask_token,
            }   )

       if self.offset > 0:
        # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
        # mask_token_sent is already added to list -> so start at 1
        self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1				,			self.offset - 1   )}   )

       snake_case__  :    Dict							       ={v: k for k, v in self.encoder.items()}
      @property
      def 						lowercase__					(     self   ):
       return len(self.sp_model   ) + self.offset
      def 						lowercase__					(     self   ):
       snake_case__  :    Dict							       ={self.convert_ids_to_tokens(_lowerCamelCase   ): i for i in range(self.vocab_size   )}
       vocab.update(self.added_tokens_encoder   )
       return vocab
      def __getstate__(     self   ):
       snake_case__  :    Union[str, Any]							       =self.__dict__.copy()
       snake_case__  :    str							       =None
       return state
      def __setstate__(     self				,			a   ):
       snake_case__  :    List[str]							       =d

       # for backward compatibility
       if not hasattr(self				,			"""sp_model_kwargs"""   ):
        snake_case__  :    Optional[int]							       ={}

       snake_case__  :    Optional[Any]							       =spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.Load(self.vocab_file   )
      def 						lowercase__					(     self				,			a   ):
       return self.sp_model.encode(_lowerCamelCase				,			out_type=_lowerCamelCase   )
      def 						lowercase__					(     self				,			a   ):
       if token in self.decoder:
        return self.decoder[token]
       elif token in self.added_tokens_decoder:
        return self.added_tokens_decoder[token]
       snake_case__  :    Optional[Any]							       =self.sp_model.piece_to_id(_lowerCamelCase   )
       return sp_id + self.offset
      def 						lowercase__					(     self				,			a   ):
       if index in self.encoder:
        return self.encoder[index]
       elif index in self.added_tokens_encoder:
        return self.added_tokens_encoder[index]
       else:
        snake_case__  :    str							       =self.sp_model.IdToPiece(index - self.offset   )
       return token
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Optional[Any]							       =[]
       snake_case__  :    Optional[int]							       =""""""
       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(_lowerCamelCase   ) + token
         snake_case__  :    Union[str, Any]							       =[]
        else:
         current_sub_tokens.append(_lowerCamelCase   )
       out_string += self.sp_model.decode(_lowerCamelCase   )
       return out_string.strip()
      def 						lowercase__					(     self				,			a=False   ):
       return 1
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    int							       =set(self.all_special_ids   )  # call it once instead of inside list comp
       all_special_ids.remove(self.unk_token_id   )  # <unk> is only sometimes special

       return [1 if x in all_special_ids else 0 for x in seq]
      def 						lowercase__					(     self				,			a				,			a = None				,			a = False   ):
       if already_has_special_tokens:
        return self._special_token_mask(_lowerCamelCase   )
       elif token_ids_a is None:
        return self._special_token_mask(_lowerCamelCase   ) + [1]
       else:
        return self._special_token_mask(token_ids_a + token_ids_a   ) + [1]
      def 						lowercase__					(     self				,			a				,			a=None   ):
       if token_ids_a is None:
        return token_ids_a + [self.eos_token_id]
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return token_ids_a + token_ids_a + [self.eos_token_id]



      def 						lowercase__					(     self				,			a				,			a = None   ):
       if not os.path.isdir(_lowerCamelCase   ):
        logger.error(F"Vocabulary path ({save_directory}) should be a directory"   )
        return
       snake_case__  :    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:
         snake_case__  :    int							       =self.sp_model.serialized_model_proto()
         fi.write(_lowerCamelCase   )

       return (out_vocab_file,)













from string import ascii_uppercase

__lowerCamelCase	:		List[Any]										=   {str(ord(c) - 55): c for c in ascii_uppercase}







def 			A__							(      _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 if isinstance(_a		,  _a	):
  raise TypeError("""int() can't convert non-string with explicit base"""	)
 if num < 0:
  raise ValueError("""parameter must be positive int"""	)
 if isinstance(_a		,  _a	):
  raise TypeError("""'str' object cannot be interpreted as an integer"""	)
 if isinstance(_a		,  _a	):
  raise TypeError("""'float' object cannot be interpreted as an integer"""	)
 if base in (0, 1):
  raise ValueError("""base must be >= 2"""	)
 if base > 36:
  raise ValueError("""base must be <= 36"""	)
 snake_case__  :    Union[str, Any]							       =""""""
 snake_case__  :    List[str]							       =0
 snake_case__  :    List[Any]							       =0
 while div != 1:
  snake_case__       ,   snake_case__  :    Union[str, Any]							       =divmod(_a		,  _a	)
  if base >= 11 and 9 < mod < 36:
   snake_case__  :    Dict							       =ALPHABET_VALUES[str(_a	)]
  else:
   snake_case__  :    Optional[Any]							       =str(_a	)
  new_value += actual_value
  snake_case__  :    Dict							       =num // base
  snake_case__  :    Tuple							       =div
  if div == 0:
   return str(new_value[::-1]	)
  elif div == 1:
   new_value += str(_a	)
   return str(new_value[::-1]	)

 return new_value[::-1]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       for base in range(2, 37):
              for num in range(10_00):
                     assert int(decimal_to_any(num, base), base) == num, (
                         num,
                         base,
                         decimal_to_any(num, base),
                         int(decimal_to_any(num, base), base),
                     )





