"""simple docstring"""


import os
from typing import List, Optional, Union

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


UpperCAmelCase__     						=  logging.get_logger(__name__)

UpperCAmelCase__     						=  {"""vocab_file""": """vocab.txt"""}

UpperCAmelCase__     						=  {
    """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""",
    },
}

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






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""





  with open(SCREAMING_SNAKE_CASE_      ,"""r"""						) as f:
    _UpperCAmelCase         =					f.read().splitlines()
    return [l.strip() for l in lines]


class 				a (						__snake_case      ):
    _snake_case   :     Optional[Any]   							=							VOCAB_FILES_NAMES
    _snake_case   :     Union[str, Any]   							=							PRETRAINED_VOCAB_FILES_MAP
    _snake_case   :     Tuple   							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _snake_case   :     Any   							=							["""input_ids""", """attention_mask"""]


    def __init__(    self		:     Dict      , __lowerCAmelCase		:     Union[str, Any]      , __lowerCAmelCase		:     Optional[Any]="<unk>"      , __lowerCAmelCase		:     str="<cls>"      , __lowerCAmelCase		:     Optional[int]="<pad>"      , __lowerCAmelCase		:     Union[str, Any]="<mask>"      , __lowerCAmelCase		:     Optional[int]="<eos>"      , **__lowerCAmelCase		:     Union[str, Any]      , ):
      super().__init__(**__lowerCAmelCase    )
      _UpperCAmelCase         =					load_vocab_file(__lowerCAmelCase    )
      _UpperCAmelCase         =					dict(enumerate(self.all_tokens    )    )
      _UpperCAmelCase         =					{tok: ind for ind, tok in enumerate(self.all_tokens    )}
      _UpperCAmelCase         =					unk_token
      _UpperCAmelCase         =					cls_token
      _UpperCAmelCase         =					pad_token
      _UpperCAmelCase         =					mask_token
      _UpperCAmelCase         =					eos_token
      _UpperCAmelCase         =					self.all_tokens
      self._create_trie(self.unique_no_split_tokens    )


    def    lowerCAmelCase_     (    self		:     int      , __lowerCAmelCase		:     Optional[int]    ):
      return self._id_to_token.get(__lowerCAmelCase      , self.unk_token    )


    def    lowerCAmelCase_     (    self		:     str      , __lowerCAmelCase		:     Any    ):
      return self._token_to_id.get(__lowerCAmelCase      , self._token_to_id.get(self.unk_token    )    )


    def    lowerCAmelCase_     (    self		:     Optional[int]      , __lowerCAmelCase		:     Any      , **__lowerCAmelCase		:     Dict    ):
      return text.split()


    def    lowerCAmelCase_     (    self		:     str      , __lowerCAmelCase		:     Optional[int]=False    ):
      return len(self._id_to_token    )


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


    def    lowerCAmelCase_     (    self		:     str      , __lowerCAmelCase		:     Union[str, Any]    ):
      return self._token_to_id.get(__lowerCAmelCase      , self._token_to_id.get(self.unk_token    )    )


    def    lowerCAmelCase_     (    self		:     str      , __lowerCAmelCase		:     Optional[int]    ):
      return self._id_to_token.get(__lowerCAmelCase      , self.unk_token    )


    def    lowerCAmelCase_     (    self		:     List[str]      , __lowerCAmelCase		:     List[str]      , __lowerCAmelCase		:     List[str] = None    ):
      _UpperCAmelCase         =					[self.cls_token_id]
      _UpperCAmelCase         =					[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    lowerCAmelCase_     (    self		:     str      , __lowerCAmelCase		:     List[str]      , __lowerCAmelCase		:     Optional[Any] = None      , __lowerCAmelCase		:     Optional[int] = False    ):
      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]
      _UpperCAmelCase         =					[1] + ([0] * len(__lowerCAmelCase    )) + [1]
      if token_ids_a is not None:
        mask += [0] * len(__lowerCAmelCase    ) + [1]
      return mask


    def    lowerCAmelCase_     (    self		:     str      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     List[Any]    ):
      _UpperCAmelCase         =					os.path.join(__lowerCAmelCase      , (filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt"""    )
      with open(__lowerCAmelCase      , """w"""    ) as f:
        f.write("""\n""".join(self.all_tokens    )    )
      return (vocab_file,)


    @property
    def    lowerCAmelCase_     (    self		:     str    ):
      return self.get_vocab_size(with_added_tokens=__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     int      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     int = False    ):
      return super()._add_tokens(__lowerCAmelCase      , special_tokens=__lowerCAmelCase    )



import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


snake_case_			  = logging.get_logger(__name__)


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    def __init__(      self						,   *a						,   **a):
         warnings.warn(
             'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use FlavaImageProcessor instead.'						,   a						,   )
         super().__init__(*a						,   **a)

