





import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


lowerCAmelCase_   =   get_logger(__name__)




def 						A_			(  lowercase_      ,		lowercase_  )			->						Any:
       if text.startswith(lowercase_  ):
              return text[len(lowercase_  ) :]
       return text  # or whatever




def 						A_			(  lowercase_  )			->						Optional[Any]:
       return {remove_prefix(lowercase_      ,		'''model.'''  ): v for k, v in sd.items()}




def 						A_			(  lowercase_  )			->						Optional[Any]:
       _snake_case      :							Dict     				=						{}
       for k in state_dicts[0].keys():
              _snake_case      :							Dict     				=						[sd[k] for sd in state_dicts]
              _snake_case      :							Tuple     				=						sum(lowercase_  ) / len(lowercase_  )
              assert isinstance(lowercase_      ,		torch.Tensor  )
              _snake_case      :							List[str]     				=						new_t
       return new_sd




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						None:
       _snake_case      :							int     				=						AutoModelForSeqaSeqLM.from_pretrained(lowercase_  )
       if os.path.isfile(lowercase_  ):
              _snake_case      :							Any     				=						[pl_ckpt_path]
       else:
              assert os.path.isdir(lowercase_  )
              _snake_case      :							List[Any]     				=						list(Path(lowercase_  ).glob('''*.ckpt'''  )  )
              assert ckpt_files, f'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

       if len(lowercase_  ) > 1:
              logger.info(f'''averaging the weights of {ckpt_files}'''  )

       _snake_case      :							List[Any]     				=						[sanitize(torch.load(lowercase_      ,		map_location='''cpu'''  )['''state_dict''']  ) for x in ckpt_files]
       _snake_case      :							List[Any]     				=						average_state_dicts(lowercase_  )

       _snake_case				,    _snake_case      :							int     				=						hf_model.load_state_dict(lowercase_      ,		strict=lowercase_  )
       assert not missing, f'''missing keys: {missing}'''
       hf_model.save_pretrained(lowercase_  )
       try:
              _snake_case      :							str     				=						AutoTokenizer.from_pretrained(lowercase_  )
              tok.save_pretrained(lowercase_  )
       except Exception:
              pass
              # dont copy tokenizer if cant


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













def 						A_			(  lowercase_  )			->						list:
       if len(lowercase_  ) <= 1:
              return data
       else:
              return [
                  *quick_sort([e for e in data[1:] if e <= data[0]]  ),
                  data[0],
                  *quick_sort([e for e in data[1:] if e > data[0]]  ),
              ]


if __name__ == "__main__":
   import doctest

   doctest.testmod()






