

"""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							=							logging.get_logger(__name__)

_a							=							{'vocab_file': 'vocab.txt'}

_a							=							{
    '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							=							{
    'facebook/esm2_t6_8M_UR50D': 10_24,
    'facebook/esm2_t12_35M_UR50D': 10_24,
}




def   _A  (  UpperCamelCase_					:	Optional[Any])     ->						List[Any]:
   '''simple docstring'''


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




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : Union[str, Any]       		=     VOCAB_FILES_NAMES
 __UpperCAmelCase     : List[str]       		=     PRETRAINED_VOCAB_FILES_MAP
 __UpperCAmelCase     : Tuple       		=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __UpperCAmelCase     : str       		=     ["input_ids", "attention_mask"]


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


 def  _lowercase			(      self							:      List[str], UpperCAmelCase__							:      int     ):
    return self._id_to_token.get(UpperCAmelCase__, self.unk_token     )


 def  _lowercase			(      self							:      List[str], UpperCAmelCase__							:      str     ):
    return self._token_to_id.get(UpperCAmelCase__, self._token_to_id.get(self.unk_token     )     )


 def  _lowercase			(      self							:      str, UpperCAmelCase__							:      Tuple, **UpperCAmelCase__							:      List[str]     ):
    return text.split()


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


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


 def  _lowercase			(      self							:      List[str], UpperCAmelCase__							:      str     ):
    return self._token_to_id.get(UpperCAmelCase__, self._token_to_id.get(self.unk_token     )     )


 def  _lowercase			(      self							:      Optional[Any], UpperCAmelCase__							:      int     ):
    return self._id_to_token.get(UpperCAmelCase__, self.unk_token     )


 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      List[int], UpperCAmelCase__							:      Optional[List[int]] = None     ):
    __lowercase								=						[self.cls_token_id]
    __lowercase								=						[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							:      Union[str, Any], UpperCAmelCase__							:      List, UpperCAmelCase__							:      Optional[List] = None, UpperCAmelCase__							:      bool = 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]
    __lowercase								=						[1] + ([0] * len(UpperCAmelCase__     )) + [1]
    if token_ids_a is not None:
       mask += [0] * len(UpperCAmelCase__     ) + [1]
    return mask


 def  _lowercase			(      self							:      Tuple, UpperCAmelCase__							:      Any, UpperCAmelCase__							:      Tuple     ):
    __lowercase								=						os.path.join(UpperCAmelCase__, (filename_prefix + "-" if filename_prefix else "") + "vocab.txt"     )
    with open(UpperCAmelCase__, "w"     ) as f:
       f.write("\n".join(self.all_tokens     )     )
    return (vocab_file,)


 @property
 def  _lowercase			(      self							:      Optional[Any]     ):
    return self.get_vocab_size(with_added_tokens=UpperCAmelCase__     )



 def  _lowercase			(      self							:      Any, UpperCAmelCase__							:      Union[List[str], List[AddedToken]], UpperCAmelCase__							:      bool = False     ):
    return super()._add_tokens(UpperCAmelCase__, special_tokens=UpperCAmelCase__     )







"""simple docstring"""





import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	str,      **UpperCamelCase_					:	Optional[int])     ->						Tuple:
   '''simple docstring'''


   __lowercase								=						AutoConfig.from_pretrained(UpperCamelCase_,      **UpperCamelCase_)
   __lowercase								=						AutoModelForSeqaSeqLM.from_config(UpperCamelCase_)
   model.save_pretrained(UpperCamelCase_)
   AutoTokenizer.from_pretrained(UpperCamelCase_).save_pretrained(UpperCamelCase_)
   return model


if __name__ == "__main__":
   fire.Fire(save_randomly_initialized_version)




