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": 10_24,
    "facebook/esm2_t12_35M_UR50D": 10_24,
}


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





class      lowerCamelCase    (UpperCAmelCase__    ):
  """simple docstring"""







  lowerCamelCase__   =  VOCAB_FILES_NAMES
  lowerCamelCase__   =  PRETRAINED_VOCAB_FILES_MAP
  lowerCamelCase__   =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCamelCase__   =  ['input_ids', 'attention_mask']







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







  def       __A   (							self       :				Optional[Any]     ,   __magic_name__       :				Tuple      )     ->		str:
        return self._id_to_token.get(_SCREAMING_SNAKE_CASE     ,   self.unk_token      )







  def       __A   (							self       :				Optional[int]     ,   __magic_name__       :				Tuple      )     ->		int:
        return self._token_to_id.get(_SCREAMING_SNAKE_CASE     ,   self._token_to_id.get(self.unk_token      )      )







  def       __A   (							self       :				Tuple     ,   __magic_name__       :				Optional[int]     ,   **__magic_name__       :				List[Any]      )     ->		Optional[Any]:
        return text.split()







  def       __A   (							self       :				Union[str, Any]     ,   __magic_name__       :				Tuple=False      )     ->		Union[str, Any]:
        return len(self._id_to_token      )







  def       __A   (							self       :				Optional[Any]      )     ->		Dict:
        return {token: i for i, token in enumerate(self.all_tokens      )}







  def       __A   (							self       :				Union[str, Any]     ,   __magic_name__       :				Dict      )     ->		int:
        return self._token_to_id.get(_SCREAMING_SNAKE_CASE     ,   self._token_to_id.get(self.unk_token      )      )







  def       __A   (							self       :				str     ,   __magic_name__       :				Dict      )     ->		str:
        return self._id_to_token.get(_SCREAMING_SNAKE_CASE     ,   self.unk_token      )







  def       __A   (							self       :				Union[str, Any]     ,   __magic_name__       :				Optional[int]     ,   __magic_name__       :				Dict = None      )     ->		List[int]:
        SCREAMING_SNAKE_CASE_      =     [self.cls_token_id]
        SCREAMING_SNAKE_CASE_      =     [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       __A   (							self       :				List[Any]     ,   __magic_name__       :				int     ,   __magic_name__       :				List[Any] = None     ,   __magic_name__       :				Optional[Any] = False      )     ->		List[int]:




        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]
        SCREAMING_SNAKE_CASE_      =     [1] + ([0] * len(_SCREAMING_SNAKE_CASE      )) + [1]
        if token_ids_a is not None:
              mask += [0] * len(_SCREAMING_SNAKE_CASE      ) + [1]
        return mask







  def       __A   (							self       :				List[str]     ,   __magic_name__       :				Optional[Any]     ,   __magic_name__       :				List[str]      )     ->		Optional[int]:
        SCREAMING_SNAKE_CASE_      =     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       __A   (							self       :				List[Any]      )     ->		int:
        return self.get_vocab_size(with_added_tokens=_SCREAMING_SNAKE_CASE      )







  def       __A   (							self       :				Union[str, Any]     ,   __magic_name__       :				List[Any]     ,   __magic_name__       :				List[Any] = False      )     ->		int:
        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__)
