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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
  _lowerCamelCase        =None


_lowerCamelCase        =logging.get_logger(__name__)

_lowerCamelCase        ={"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_lowerCamelCase        ={
    """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"""
        ),
    },
    """tokenizer_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"""
        ),
    },
}

_lowerCamelCase        ={
    """xlm-roberta-base""": 5_1_2,
    """xlm-roberta-large""": 5_1_2,
    """xlm-roberta-large-finetuned-conll02-dutch""": 5_1_2,
    """xlm-roberta-large-finetuned-conll02-spanish""": 5_1_2,
    """xlm-roberta-large-finetuned-conll03-english""": 5_1_2,
    """xlm-roberta-large-finetuned-conll03-german""": 5_1_2,
}





class 							A__		(     __SCREAMING_SNAKE_CASE):
      _UpperCAmelCase    :   str   	=      VOCAB_FILES_NAMES
      _UpperCAmelCase    :   Optional[Any]   	=      PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase    :   List[str]   	=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase    :   Any   	=      ["""input_ids""", """attention_mask"""]
      _UpperCAmelCase    :   Optional[int]   	=      XLMRobertaTokenizer
      def __init__(				self   ,  __magic_name__=None   ,  __magic_name__=None   ,  __magic_name__="<s>"   ,  __magic_name__="</s>"   ,  __magic_name__="</s>"   ,  __magic_name__="<s>"   ,  __magic_name__="<unk>"   ,  __magic_name__="<pad>"   ,  __magic_name__="<mask>"   ,  **__magic_name__   ,  ):
       # Mask token behave like a normal word, i.e. include the space before it
       lowerCamelCase		:     Dict          =				AddedToken(__magic_name__   ,  lstrip=__magic_name__   ,  rstrip=__magic_name__				) if isinstance(__magic_name__   ,  __magic_name__				) else mask_token

       super().__init__(
           __magic_name__   ,  tokenizer_file=__magic_name__   ,  bos_token=__magic_name__   ,  eos_token=__magic_name__   ,  sep_token=__magic_name__   ,  cls_token=__magic_name__   ,  unk_token=__magic_name__   ,  pad_token=__magic_name__   ,  mask_token=__magic_name__   ,  **__magic_name__   ,  )

       lowerCamelCase		:     List[Any]          =				vocab_file
       lowerCamelCase		:     List[Any]          =				False if not self.vocab_file else True
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = None				):

       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       lowerCamelCase		:     List[str]          =				[self.cls_token_id]
       lowerCamelCase		:     Optional[int]          =				[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = None				):
       lowerCamelCase		:     Optional[int]          =				[self.sep_token_id]
       lowerCamelCase		:     int          =				[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]






      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__ = None				):
       if not self.can_save_slow_tokenizer:
        raise ValueError(
            """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
            """tokenizer."""				)

       if not os.path.isdir(__magic_name__				):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''				)
        return
       lowerCamelCase		:     Any          =				os.path.join(
           __magic_name__   ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

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

       return (out_vocab_file,)






def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
 lowerCamelCase		:     Optional[int]          =				[1 / first_term]
 lowerCamelCase		:     Any          =				1 / first_term
 for _ in range(number_of_terms - 1							):
  first_term += common_difference
  arithmetic_progression.append(lowerCamelCase							)
 lowerCamelCase		:     Optional[Any]          =				[1 / step for step in arithmetic_progression]
 return sum(lowerCamelCase							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(sum_of_harmonic_progression(1 / 2, 2, 2))




