



"""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


A:   str								=		logging.get_logger(__name__)

A:   List[Any]								=		{"vocab_file": "vocab.txt"}

A:   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",
    },
}

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

def       _snake_case						(						UpperCamelCase						:		int						):
 with open(UpperCamelCase			,		"""r"""						) as f:
  UpperCAmelCase	:					int	=	f.read().splitlines()
  return [l.strip() for l in lines]



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Union[str, Any]						=						VOCAB_FILES_NAMES
   __lowerCAmelCase  :   Dict						=						PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase  :   List[Any]						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase  :   str						=						['input_ids', 'attention_mask']

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





    '''simple docstring'''




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

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      str:





    '''simple docstring'''




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

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      int:





    '''simple docstring'''




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

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    return text.split()

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE=False						)  ->      Union[str, Any]:





    '''simple docstring'''




    return len(self._id_to_token						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




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

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      int:





    '''simple docstring'''




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

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      str:





    '''simple docstring'''




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

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      List[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	[self.cls_token_id]
    UpperCAmelCase	:					Tuple	=	[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 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = 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]
    UpperCAmelCase	:					str	=	[1] + ([0] * len(_SCREAMING_SNAKE_CASE						)) + [1]
    if token_ids_a is not None:
     mask += [0] * len(_SCREAMING_SNAKE_CASE						) + [1]
    return mask

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[int]	=	os.path.join(_SCREAMING_SNAKE_CASE      ,     (filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt"""						)
    with open(_SCREAMING_SNAKE_CASE      ,     """w"""						) as f:
     f.write("""\n""".join(self.all_tokens						)						)
    return (vocab_file,)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    return self.get_vocab_size(with_added_tokens=_SCREAMING_SNAKE_CASE						)




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = False						)  ->      int:





    '''simple docstring'''




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





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


A:   Optional[Any]								=		{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[int]								=		[
          "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TapasForMaskedLM",
          "TapasForQuestionAnswering",
          "TapasForSequenceClassification",
          "TapasModel",
          "TapasPreTrainedModel",
          "load_tf_weights_in_tapas",
      ]
try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   List[str]								=		[
          "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFTapasForMaskedLM",
          "TFTapasForQuestionAnswering",
          "TFTapasForSequenceClassification",
          "TFTapasModel",
          "TFTapasPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
      from .tokenization_tapas import TapasTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tapas import (
                TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TapasForMaskedLM,
                TapasForQuestionAnswering,
                TapasForSequenceClassification,
                TapasModel,
                TapasPreTrainedModel,
                load_tf_weights_in_tapas,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_tapas import (
                TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFTapasForMaskedLM,
                TFTapasForQuestionAnswering,
                TFTapasForSequenceClassification,
                TFTapasModel,
                TFTapasPreTrainedModel,
            )


else:
      import sys

      A:   Optional[Any]								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
