





import os
from typing import List, Optional, Union

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


lowercase				:	str      						=  logging.get_logger(__name__)

lowercase				:	List[str]      						=  {"""vocab_file""": """vocab.txt"""}

lowercase				:	List[str]      						=  {
    """vocab_file""": {
        """facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
        """facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
    },
}

lowercase				:	Dict      						=  {
    """facebook/esm2_t6_8M_UR50D""": 1_0_2_4,
    """facebook/esm2_t12_35M_UR50D""": 1_0_2_4,
}



def A_     (		A__    )  ->		Tuple:
       with open(A__		,				'r'    ) as f:
              a__						:     List[Any]			=    f.read().splitlines()
              return [l.strip() for l in lines]





class 			A__   (							__UpperCAmelCase     ):




   """simple docstring"""



   __A       :    Any	  =  VOCAB_FILES_NAMES
   __A       :    Any	  =  PRETRAINED_VOCAB_FILES_MAP
   __A       :    Tuple	  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __A       :    Union[str, Any]	  =  ['''input_ids''', '''attention_mask''']

   def __init__( self ,  lowercase ,  lowercase="<unk>" ,  lowercase="<cls>" ,  lowercase="<pad>" ,  lowercase="<mask>" ,  lowercase="<eos>" ,  **lowercase ,  )      ->						str:







          '''simple docstring'''

          super().__init__(**lowercase)
          a__						:     Tuple			=    load_vocab_file(lowercase)
          a__						:     Tuple			=    dict(enumerate(self.all_tokens))
          a__						:     int			=    {tok: ind for ind, tok in enumerate(self.all_tokens)}
          a__						:     List[str]			=    unk_token
          a__						:     int			=    cls_token
          a__						:     Dict			=    pad_token
          a__						:     int			=    mask_token
          a__						:     List[str]			=    eos_token
          a__						:     int			=    self.all_tokens
          self._create_trie(self.unique_no_split_tokens)

   def 							__lowercase		( self ,  lowercase)      ->						str:







          '''simple docstring'''

          return self._id_to_token.get(lowercase ,  self.unk_token)

   def 							__lowercase		( self ,  lowercase)      ->						int:







          '''simple docstring'''

          return self._token_to_id.get(lowercase ,  self._token_to_id.get(self.unk_token))

   def 							__lowercase		( self ,  lowercase ,  **lowercase)      ->						Any:







          '''simple docstring'''

          return text.split()

   def 							__lowercase		( self ,  lowercase=False)      ->						Tuple:







          '''simple docstring'''

          return len(self._id_to_token)

   def 							__lowercase		( self)      ->						List[Any]:







          '''simple docstring'''

          return {token: i for i, token in enumerate(self.all_tokens)}

   def 							__lowercase		( self ,  lowercase)      ->						int:







          '''simple docstring'''

          return self._token_to_id.get(lowercase ,  self._token_to_id.get(self.unk_token))

   def 							__lowercase		( self ,  lowercase)      ->						str:







          '''simple docstring'''

          return self._id_to_token.get(lowercase ,  self.unk_token)

   def 							__lowercase		( self ,  lowercase ,  lowercase = None)      ->						List[int]:







          '''simple docstring'''

          a__						:     int			=    [self.cls_token_id]
          a__						:     Union[str, Any]			=    [self.eos_token_id]  # No sep token in ESM vocabulary
          if token_ids_a is None:
                 if self.eos_token_id is None:
                        return cls + token_ids_a
                 else:
                        return cls + token_ids_a + sep
          elif self.eos_token_id is None:
                 raise ValueError('Cannot tokenize multiple sequences when EOS token is not set!')
          return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token

   def 							__lowercase		( self ,  lowercase ,  lowercase = None ,  lowercase = False)      ->						List[int]:







          '''simple docstring'''

          if already_has_special_tokens:
                 if token_ids_a is not None:
                        raise ValueError(
                            'You should not supply a second sequence if the provided sequence of '
                            'ids is already formatted with special tokens for the model.')

                 return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
          a__						:     Tuple			=    [1] + ([0] * len(lowercase)) + [1]
          if token_ids_a is not None:
                 mask += [0] * len(lowercase) + [1]
          return mask

   def 							__lowercase		( self ,  lowercase ,  lowercase)      ->						List[str]:







          '''simple docstring'''

          a__						:     str			=    os.path.join(lowercase ,  (filename_prefix + '-' if filename_prefix else '') + 'vocab.txt')
          with open(lowercase ,  'w') as f:
                 f.write('\n'.join(self.all_tokens))
          return (vocab_file,)

   @property
   def 							__lowercase		( self)      ->						int:







          '''simple docstring'''

          return self.get_vocab_size(with_added_tokens=lowercase)

   def 							__lowercase		( self ,  lowercase ,  lowercase = False)      ->						int:







          '''simple docstring'''

          return super()._add_tokens(lowercase ,  special_tokens=lowercase)









def A_     (		A__    )  ->		int:

       if not isinstance(A__		,				A__    ):
              raise TypeError('only integers accepted as input'    )
       else:
              a__						:     List[Any]			=    str(abs(A__    )    )
              a__						:     Optional[int]			=    [list(A__    ) for char in range(len(A__    )    )]
              for index in range(len(A__    )    ):
                     num_transpositions[index].pop(A__    )
              return max(
                  int(''.join(list(A__    )    )    ) for transposition in num_transpositions    )


if __name__ == "__main__":
    __import__("""doctest""").testmod()


