
"""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__      :	Optional[Any]          =    logging.get_logger(__name__)

A__      :	Union[str, Any]          =    {"""vocab_file""": """vocab.txt"""}

A__      :	Dict          =    {
    """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__      :	str          =    {
    """facebook/esm2_t6_8M_UR50D""": 1_024,
    """facebook/esm2_t12_35M_UR50D""": 1_024,
}



def 							a__ (				lowerCAmelCase	:       Dict		):






     '''simple docstring'''
     with open(lowerCAmelCase       ,    "r"		) as f:
          UpperCAmelCase__							: Optional[Any] 							=						f.read().splitlines()
          return [l.strip() for l in lines]







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





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



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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					str:
          return self._id_to_token.get(__UpperCamelCase ,			self.unk_token  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					int:
          return self._token_to_id.get(__UpperCamelCase ,			self._token_to_id.get(self.unk_token  )  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			**__UpperCamelCase  )->					Any:
          return text.split()



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase=False  )->					List[str]:
          return len(self._id_to_token  )



     def  lowerCAmelCase__       (      self  )->					Tuple:
          return {token: i for i, token in enumerate(self.all_tokens  )}



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					int:
          return self._token_to_id.get(__UpperCamelCase ,			self._token_to_id.get(self.unk_token  )  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					str:
          return self._id_to_token.get(__UpperCamelCase ,			self.unk_token  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					List[int]:
          UpperCAmelCase__							: List[str] 							=						[self.cls_token_id]
          UpperCAmelCase__							: int 							=						[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 ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = 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]
          UpperCAmelCase__							: Any 							=						[1] + ([0] * len(__UpperCamelCase  )) + [1]
          if token_ids_a is not None:
               mask += [0] * len(__UpperCamelCase  ) + [1]
          return mask



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase  )->					Optional[Any]:
          UpperCAmelCase__							: Dict 							=						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  lowerCAmelCase__       (      self  )->					int:
          return self.get_vocab_size(with_added_tokens=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = False  )->					int:
          return super()._add_tokens(__UpperCamelCase ,			special_tokens=__UpperCamelCase  )





"""simple docstring"""




import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


A__      :	Optional[int]          =    ["""small""", """medium""", """large"""]

A__      :	Optional[int]          =    """lm_head.decoder.weight"""
A__      :	Dict          =    """lm_head.weight"""



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       str		):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						torch.load(lowerCAmelCase		)
     UpperCAmelCase__							: Optional[int] 							=						d.pop(lowerCAmelCase		)
     os.makedirs(lowerCAmelCase       ,    exist_ok=lowerCAmelCase		)
     torch.save(lowerCAmelCase       ,    os.path.join(lowerCAmelCase       ,    lowerCAmelCase		)		)


if __name__ == "__main__":
    A__      :	List[Any]          =    argparse.ArgumentParser()
    parser.add_argument("""--dialogpt_path""", default=""".""", type=str)
    A__      :	Tuple          =    parser.parse_args()
    for MODEL in DIALOGPT_MODELS:
        A__      :	Tuple          =    os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
        A__      :	str          =    f"""./DialoGPT-{MODEL}"""
        convert_dialogpt_checkpoint(
            checkpoint_path,
            pytorch_dump_folder_path,
        )



