




from __future__ import annotations





class  lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self						:	int       ,       _lowerCAmelCase						:	int		):
       SCREAMING_SNAKE_CASE_					=    value
       SCREAMING_SNAKE_CASE_					=    None
       SCREAMING_SNAKE_CASE_					=    None





class  lowerCamelCase_							:


 '''simple docstring'''







 lowercase_       					=					42



 def __init__(							self						:	str		):
       SCREAMING_SNAKE_CASE_					=    0



 def    lowerCAmelCase_						(							self						:	Optional[int]       ,       _lowerCAmelCase						:	Node | None       ,       _lowerCAmelCase						:	int		):
       if node is None:
             return

       if path_sum == self.target:
             self.paths += 1

       if node.left:
             self.depth_first_search(node.left       ,       path_sum + node.left.value		)
       if node.right:
             self.depth_first_search(node.right       ,       path_sum + node.right.value		)




 def    lowerCAmelCase_						(							self						:	str       ,       _lowerCAmelCase						:	Node | None       ,       _lowerCAmelCase						:	int | None = None		):
       if node is None:
             return 0
       if target is not None:
             SCREAMING_SNAKE_CASE_					=    target

       self.depth_first_search(_lowerCAmelCase       ,       node.value		)
       self.path_sum(node.left		)
       self.path_sum(node.right		)

       return self.paths


if __name__ == "__main__":
   import doctest

   doctest.testmod()





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__				:     Optional[Any]			       =							get_logger(__name__)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			str							)      ->				List[str]:
      if text.startswith(__UpperCAmelCase							):
            return text[len(__UpperCAmelCase							) :]
      return text  # or whatever

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Union[str, Any]							)      ->				List[Any]:
      return {remove_prefix(__UpperCAmelCase    ,    'model.'							): v for k, v in sd.items()}

def       UpperCAmelCase_							(   __UpperCAmelCase		:			List[Dict[str, torch.Tensor]]							)      ->				Dict:
      SCREAMING_SNAKE_CASE_					=    {}
      for k in state_dicts[0].keys():
            SCREAMING_SNAKE_CASE_					=    [sd[k] for sd in state_dicts]
            SCREAMING_SNAKE_CASE_					=    sum(__UpperCAmelCase							) / len(__UpperCAmelCase							)
            assert isinstance(__UpperCAmelCase    ,    torch.Tensor							)
            SCREAMING_SNAKE_CASE_					=    new_t
      return new_sd

def       UpperCAmelCase_							(   __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			str							)      ->				None:
      SCREAMING_SNAKE_CASE_					=    AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase							)
      if os.path.isfile(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_					=    [pl_ckpt_path]
      else:
            assert os.path.isdir(__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_					=    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}"							)

      SCREAMING_SNAKE_CASE_					=    [sanitize(torch.load(__UpperCAmelCase    ,    map_location='cpu'							)['state_dict']							) for x in ckpt_files]
      SCREAMING_SNAKE_CASE_					=    average_state_dicts(__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					=    hf_model.load_state_dict(__UpperCAmelCase    ,    strict=__UpperCAmelCase							)
      assert not missing, f"missing keys: {missing}"
      hf_model.save_pretrained(__UpperCAmelCase							)
      try:
            SCREAMING_SNAKE_CASE_					=    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)