
"""simple docstring"""




import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A__      :	str          =    logging.get_logger(__name__)

A__      :	List[Any]          =    {"""vocab_file""": """vocab.json"""}

A__      :	int          =    {
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

A__      :	List[Any]          =    {"""mgp-str""": 27}







class   _lowercase						(       lowercase_				):


     '''simple docstring'''





     _A								=							VOCAB_FILES_NAMES
     _A								=							PRETRAINED_VOCAB_FILES_MAP
     _A								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase="[GO]" ,			__UpperCamelCase="[GO]" ,			__UpperCamelCase="[s]" ,			__UpperCamelCase="[GO]" ,			**__UpperCamelCase  )->					Union[str, Any]:
          super().__init__(
              unk_token=lowerCamelCase_ ,			bos_token=lowerCamelCase_ ,			eos_token=lowerCamelCase_ ,			pad_token=lowerCamelCase_ ,			**lowerCamelCase_ ,			)

          with open(lowerCamelCase_ ,			encoding="utf-8"  ) as vocab_handle:
               UpperCAmelCase__							: Union[str, Any] 							=						json.load(lowerCamelCase_  )
          UpperCAmelCase__							: Dict 							=						{v: k for k, v in self.vocab.items()}



     @property
     def  lowerCAmelCase__       (      self  )->					Tuple:
          return len(self.vocab  )



     def  lowerCAmelCase__       (      self  )->					str:
          return dict(self.vocab ,			**self.added_tokens_encoder  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[Any]:
          UpperCAmelCase__							: int 							=						[]
          for s in text:
               char_tokens.extend(lowerCamelCase_  )
          return char_tokens



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Any:
          return self.vocab.get(lowerCamelCase_ ,			self.vocab.get(self.unk_token  )  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					str:
          return self.decoder.get(lowerCamelCase_  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					Optional[Any]:



          if not os.path.isdir(lowerCamelCase_  ):
               logger.error("Vocabulary path ({}) should be a directory".format(lowerCamelCase_  )  )
               return
          UpperCAmelCase__							: List[str] 							=						os.path.join(
              lowerCamelCase_ ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

          with open(lowerCamelCase_ ,			"w" ,			encoding="utf-8"  ) as f:
               f.write(json.dumps(self.vocab ,			indent=2 ,			sort_keys=lowerCamelCase_ ,			ensure_ascii=lowerCamelCase_  ) + "\n"  )

          return (vocab_file,)





"""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,
        )



