






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


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

__lowerCamelCase	:		Optional[int]										=   """▁"""

__lowerCamelCase	:		Tuple										=   {"""vocab_file""": """sentencepiece.bpe.model"""}

__lowerCamelCase	:		int										=   {
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

__lowerCamelCase	:		Optional[int]										=   {
    """xlm-roberta-base""": 5_12,
    """xlm-roberta-large""": 5_12,
    """xlm-roberta-large-finetuned-conll02-dutch""": 5_12,
    """xlm-roberta-large-finetuned-conll02-spanish""": 5_12,
    """xlm-roberta-large-finetuned-conll03-english""": 5_12,
    """xlm-roberta-large-finetuned-conll03-german""": 5_12,
}







class  _lowercase   (   _A						):
      _a							:						Tuple										=							VOCAB_FILES_NAMES
      _a							:						Optional[Any]										=							PRETRAINED_VOCAB_FILES_MAP
      _a							:						List[Any]										=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _a							:						Optional[int]										=							['input_ids', 'attention_mask']
      def __init__(     self				,			a				,			a="<s>"				,			a="</s>"				,			a="</s>"				,			a="<s>"				,			a="<unk>"				,			a="<pad>"				,			a="<mask>"				,			a = None				,			**a				,			):
       # Mask token behave like a normal word, i.e. include the space before it
       snake_case__  :    List[Any]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else mask_token

       snake_case__  :    Optional[int]							       ={} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=a				,			eos_token=a				,			unk_token=a				,			sep_token=a				,			cls_token=a				,			pad_token=a				,			mask_token=a				,			sp_model_kwargs=self.sp_model_kwargs				,			**a				,			)

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

       # Original fairseq vocab and spm vocab must be "aligned":
       # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
       # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
       # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
       # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

       # Mimic fairseq token-to-id alignment for the first 4 token
       snake_case__  :    Dict							       ={"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

       # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
       snake_case__  :    Tuple							       =1

       snake_case__  :    List[Any]							       =len(self.sp_model   ) + self.fairseq_offset
       snake_case__  :    Optional[Any]							       ={v: k for k, v in self.fairseq_tokens_to_ids.items()}
      def __getstate__(     self   ):
       snake_case__  :    Union[str, Any]							       =self.__dict__.copy()
       snake_case__  :    Any							       =None
       snake_case__  :    Union[str, Any]							       =self.sp_model.serialized_model_proto()
       return state
      def __setstate__(     self				,			a   ):
       snake_case__  :    Optional[int]							       =d

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

       snake_case__  :    str							       =spm.SentencePieceProcessor(**self.sp_model_kwargs   )
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )
      def 						lowercase__					(     self				,			a				,			a = None   ):

       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       snake_case__  :    int							       =[self.cls_token_id]
       snake_case__  :    List[Any]							       =[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep
      def 						lowercase__					(     self				,			a				,			a = None				,			a = False   ):

       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=a				,			token_ids_a=a				,			already_has_special_tokens=a   )

       if token_ids_a is None:
        return [1] + ([0] * len(a   )) + [1]
       return [1] + ([0] * len(a   )) + [1, 1] + ([0] * len(a   )) + [1]
      def 						lowercase__					(     self				,			a				,			a = None   ):
       snake_case__  :    List[str]							       =[self.sep_token_id]
       snake_case__  :    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 + sep + token_ids_a + sep   ) * [0]
      @property
      def 						lowercase__					(     self   ):
       return len(self.sp_model   ) + self.fairseq_offset + 1  # Add the <mask> token
      def 						lowercase__					(     self   ):
       snake_case__  :    str							       ={self.convert_ids_to_tokens(a   ): i for i in range(self.vocab_size   )}
       vocab.update(self.added_tokens_encoder   )
       return vocab
      def 						lowercase__					(     self				,			a   ):
       return self.sp_model.encode(a				,			out_type=a   )
      def 						lowercase__					(     self				,			a   ):
       if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
       snake_case__  :    Optional[int]							       =self.sp_model.PieceToId(a   )

       # Need to return unknown token if the SP model returned 0
       return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
      def 						lowercase__					(     self				,			a   ):
       if index in self.fairseq_ids_to_tokens:
        return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset   )
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Optional[int]							       ="""""".join(a   ).replace(a				,			""" """   ).strip()
       return out_string



      def 						lowercase__					(     self				,			a				,			a = None   ):
       if not os.path.isdir(a   ):
        logger.error(F"Vocabulary path ({save_directory}) should be a directory"   )
        return
       snake_case__  :    Dict							       =os.path.join(
           a				,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]   )

       if os.path.abspath(self.vocab_file   ) != os.path.abspath(a   ) and os.path.isfile(self.vocab_file   ):
        copyfile(self.vocab_file				,			a   )
       elif not os.path.isfile(self.vocab_file   ):
        with open(a				,			"""wb"""   ) as fi:
         snake_case__  :    Optional[int]							       =self.sp_model.serialized_model_proto()
         fi.write(a   )

       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),
                     )





