



'''simple docstring'''






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


A_				:					List[Any]							=      get_logger(__name__)




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_						)->						List[Any]:





       '''simple docstring'''


       if text.startswith(lowerCAmelCase_						):
              return text[len(lowerCAmelCase_						) :]
       return text  # or whatever




def        snake_case_   (	lowerCAmelCase_						)->						List[Any]:





       '''simple docstring'''


       return {remove_prefix(lowerCAmelCase_			,  """model."""						): v for k, v in sd.items()}




def        snake_case_   (	lowerCAmelCase_						)->						Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase    :		List[str]     	=					{}
       for k in state_dicts[0].keys():
              _UpperCAmelCase    :		List[Any]     	=					[sd[k] for sd in state_dicts]
              _UpperCAmelCase    :		List[Any]     	=					sum(lowerCAmelCase_						) / len(lowerCAmelCase_						)
              assert isinstance(lowerCAmelCase_			,  torch.Tensor						)
              _UpperCAmelCase    :		Union[str, Any]     	=					new_t
       return new_sd




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						None:





       '''simple docstring'''


       _UpperCAmelCase    :		str     	=					AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_						)
       if os.path.isfile(lowerCAmelCase_						):
              _UpperCAmelCase    :		str     	=					[pl_ckpt_path]
       else:
              assert os.path.isdir(lowerCAmelCase_						)
              _UpperCAmelCase    :		Optional[int]     	=					list(Path(lowerCAmelCase_						).glob("""*.ckpt"""						)						)
              assert ckpt_files, F'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

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

       _UpperCAmelCase    :		Any     	=					[sanitize(torch.load(lowerCAmelCase_			,  map_location="""cpu"""						)["""state_dict"""]						) for x in ckpt_files]
       _UpperCAmelCase    :		Optional[Any]     	=					average_state_dicts(lowerCAmelCase_						)

       _UpperCAmelCase	,_UpperCAmelCase    :		Dict     	=					hf_model.load_state_dict(lowerCAmelCase_			,  strict=lowerCAmelCase_						)
       assert not missing, F'''missing keys: {missing}'''
       hf_model.save_pretrained(lowerCAmelCase_						)
       try:
              _UpperCAmelCase    :		List[str]     	=					AutoTokenizer.from_pretrained(lowerCAmelCase_						)
              tok.save_pretrained(lowerCAmelCase_						)
       except Exception:
              pass
              # dont copy tokenizer if cant


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








'''simple docstring'''






import itertools




def        snake_case_   (	lowerCAmelCase_						)->						Dict:





       '''simple docstring'''


       return (
           int("""""".join(combination[0:2]						)						) * int("""""".join(combination[2:5]						)						)
           == int("""""".join(combination[5:9]						)						)
       ) or (
           int("""""".join(combination[0]						)						) * int("""""".join(combination[1:5]						)						)
           == int("""""".join(combination[5:9]						)						)
       )




def        snake_case_   (	)->						str:





       '''simple docstring'''


       return sum(
           {
               int("""""".join(pandigital[5:9]						)						)
               for pandigital in itertools.permutations("""123456789"""						)
               if is_combination_valid(lowerCAmelCase_						)
           }						)


if __name__ == "__main__":
   print(solution())



