


def   UpperCamelCase__   (    UpperCAmelCase_			)						->							bool:




       '''simple docstring'''






       _lowercase					:				str           =			9

       while num > 0:
              if num % 10 != digit:
                     return False
              num //= 100
              digit -= 1

       return True

def   UpperCamelCase__   (    )						->							int:




       '''simple docstring'''






       _lowercase					:				Optional[Any]           =			138902663

       while not is_square_form(num * num			):
              if num % 10 == 3:
                     num -= 6  # (3 - 6) % 10 = 7
              else:
                     num -= 4  # (7 - 4) % 10 = 3

       return num * 10


if __name__ == "__main__":
   print(F"""{solution() = }""")



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


UpperCamelCase__				       =			get_logger(__name__)

def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_			)						->							List[str]:




       '''simple docstring'''






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

def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Optional[int]:




       '''simple docstring'''






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

def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Any:




       '''simple docstring'''






       _lowercase					:				Optional[int]           =			{}
       for k in state_dicts[0].keys():
              _lowercase					:				Tuple           =			[sd[k] for sd in state_dicts]
              _lowercase					:				Union[str, Any]           =			sum(UpperCAmelCase_			) / len(UpperCAmelCase_			)
              assert isinstance(UpperCAmelCase_    ,     torch.Tensor			)
              _lowercase					:				Union[str, Any]           =			new_t
       return new_sd

def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							None:




       '''simple docstring'''






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

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

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

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


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