





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:
       snake_case__   :				Union[str, Any]						=	None


snake_case__   :				Tuple						=	logging.get_logger(__name__)

snake_case__   :				int						=	{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

snake_case__   :				Union[str, Any]						=	{
    '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'
        ),
    },
}

snake_case__   :				Tuple						=	{
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}





class A_   (     _lowerCamelCase  ):
   lowerCAmelCase__  	=					VOCAB_FILES_NAMES
   lowerCAmelCase__  	=					PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase__  	=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase__  	=					["""input_ids""", """attention_mask"""]
   lowerCAmelCase__  	=					XLMRobertaTokenizer




   def __init__(self      :Optional[int]       ,  _UpperCamelCase      :Any=None       ,  _UpperCamelCase      :Dict=None       ,  _UpperCamelCase      :List[Any]="<s>"       ,  _UpperCamelCase      :List[Any]="</s>"       ,  _UpperCamelCase      :Union[str, Any]="</s>"       ,  _UpperCamelCase      :Dict="<s>"       ,  _UpperCamelCase      :Any="<unk>"       ,  _UpperCamelCase      :List[str]="<pad>"       ,  _UpperCamelCase      :str="<mask>"       ,  **_UpperCamelCase      :int       ,  )->   Optional[int]:
         # Mask token behave like a normal word, i.e. include the space before it
         __A			  =							AddedToken(_UpperCamelCase       ,  lstrip=_UpperCamelCase       ,  rstrip=_UpperCamelCase					) if isinstance(_UpperCamelCase       ,  _UpperCamelCase					) else mask_token

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

         __A			  =							vocab_file
         __A			  =							False if not self.vocab_file else True




   def _lowerCAmelCase (self      :str       ,  _UpperCamelCase      :List[int]       ,  _UpperCamelCase      :Optional[List[int]] = None					)->   List[int]:

         if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         __A			  =							[self.cls_token_id]
         __A			  =							[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep




   def _lowerCAmelCase (self      :List[Any]       ,  _UpperCamelCase      :List[int]       ,  _UpperCamelCase      :Optional[List[int]] = None					)->   List[int]:
         __A			  =							[self.sep_token_id]
         __A			  =							[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 _lowerCAmelCase (self      :int       ,  _UpperCamelCase      :str       ,  _UpperCamelCase      :Optional[str] = None					)->   Tuple[str]:
         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(_UpperCamelCase					):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""					)
               return
         __A			  =							os.path.join(
             _UpperCamelCase       ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

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

         return (out_vocab_file,)












import os



def _a							(      )      ->		int:







      '''simple docstring'''






      __A			  =							os.path.dirname(os.path.realpath(lowerCamelCase					)					)
      __A			  =							os.path.join(lowerCamelCase   ,							'''triangle.txt'''					)

      with open(lowerCamelCase					) as in_file:
            __A			  =							[[int(lowerCamelCase					) for i in line.split()] for line in in_file]

      while len(lowerCamelCase					) != 1:
            __A			  =							triangle.pop()
            __A			  =							triangle[-1]
            for j in range(len(lowerCamelCase					) - 1					):
                  curr_row[j] += max(last_row[j]   ,							last_row[j + 1]					)
      return triangle[0][0]


if __name__ == "__main__":
       print(solution())





