





'''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


lowercase							       =			logging.get_logger(__name__)

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

lowercase							       =			{
    "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							       =			{
    "facebook/esm2_t6_8M_UR50D": 1_024,
    "facebook/esm2_t12_35M_UR50D": 1_024,
}






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







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







class 	UpperCAmelCase	(		UpperCAmelCase_):


 '''simple docstring'''




 __magic_name__					: str  =      VOCAB_FILES_NAMES
 __magic_name__					: int  =      PRETRAINED_VOCAB_FILES_MAP
 __magic_name__					: List[str]  =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __magic_name__					: List[str]  =      ['input_ids', 'attention_mask']




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



      """simple docstring"""







      super().__init__(**_lowercase)
      a_       		=load_vocab_file(_lowercase)
      a_       		=dict(enumerate(self.all_tokens))
      a_       		={tok: ind for ind, tok in enumerate(self.all_tokens)}
      a_       		=unk_token
      a_       		=cls_token
      a_       		=pad_token
      a_       		=mask_token
      a_       		=eos_token
      a_       		=self.all_tokens
      self._create_trie(self.unique_no_split_tokens)




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      str:



      """simple docstring"""







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




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      int:



      """simple docstring"""







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




 def       lowercase_  (   self							,  lowerCAmelCase_							,  **lowerCAmelCase_) ->      List[Any]:



      """simple docstring"""







      return text.split()




 def       lowercase_  (   self							,  lowerCAmelCase_=False) ->      List[Any]:



      """simple docstring"""







      return len(self._id_to_token)




 def       lowercase_  (   self) ->      Any:



      """simple docstring"""







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




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      int:



      """simple docstring"""







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




 def       lowercase_  (   self							,  lowerCAmelCase_) ->      str:



      """simple docstring"""







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




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None) ->      List[int]:



      """simple docstring"""







      a_       		=[self.cls_token_id]
      a_       		=[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							,  lowerCAmelCase_							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = 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_       		=[1] + ([0] * len(_lowercase)) + [1]
      if token_ids_a is not None:
           mask += [0] * len(_lowercase) + [1]
      return mask




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_) ->      Tuple:



      """simple docstring"""







      a_       		=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							,  lowerCAmelCase_							,  lowerCAmelCase_ = False) ->      int:



      """simple docstring"""







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







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase							       =			{
    '''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			[
      '''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TimesformerModel''',
      '''TimesformerForVideoClassification''',
      '''TimesformerPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timesformer import (
        TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimesformerForVideoClassification,
        TimesformerModel,
        TimesformerPreTrainedModel,
    )

else:
  import sys

  lowercase							       =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


