'''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


snake_case  =			get_logger(__name__)







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							str    ):
      if text.startswith(_lowerCamelCase    ):
            return text[len(_lowerCamelCase    ) :]
      return text  # or whatever







def 			A_   ( _lowerCamelCase			:							List[str]    ):
      return {remove_prefix(_lowerCamelCase  ,  'model.'    ): v for k, v in sd.items()}







def 			A_   ( _lowerCamelCase			:							List[Dict[str, torch.Tensor]]    ):
      _lowerCAmelCase		   =   {}
      for k in state_dicts[0].keys():
            _lowerCAmelCase		   =   [sd[k] for sd in state_dicts]
            _lowerCAmelCase		   =   sum(_lowerCamelCase    ) / len(_lowerCamelCase    )
            assert isinstance(_lowerCamelCase  ,  torch.Tensor    )
            _lowerCAmelCase		   =   new_t
      return new_sd







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							str  ,  _lowerCamelCase			:							str    ):
      _lowerCAmelCase		   =   AutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase    )
      if os.path.isfile(_lowerCamelCase    ):
            _lowerCAmelCase		   =   [pl_ckpt_path]
      else:
            assert os.path.isdir(_lowerCamelCase    )
            _lowerCAmelCase		   =   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}"    )

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

      _lowerCAmelCase   ,  _lowerCAmelCase		   =   hf_model.load_state_dict(_lowerCamelCase  ,  strict=_lowerCamelCase    )
      assert not missing, F"missing keys: {missing}"
      hf_model.save_pretrained(_lowerCamelCase    )
      try:
            _lowerCAmelCase		   =   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'''




snake_case  =			{0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

snake_case  =			{0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}







def 			A_   ( _lowerCamelCase			:							dict  ,  _lowerCamelCase			:							int  ,  _lowerCamelCase			:							list    ):
      _lowerCAmelCase		   =   True
      _lowerCAmelCase		   =   []

      for neighbour in graph[vert]:
            if not visited[neighbour]:
                  connected_verts += dfs(_lowerCamelCase  ,  _lowerCamelCase  ,  _lowerCamelCase    )

      return [vert, *connected_verts]







def 			A_   ( _lowerCamelCase			:							dict    ):
      _lowerCAmelCase		   =   len(_lowerCamelCase    )
      _lowerCAmelCase		   =   graph_size * [False]
      _lowerCAmelCase		   =   []

      for i in range(_lowerCamelCase    ):
            if not visited[i]:
                  _lowerCAmelCase		   =   dfs(_lowerCamelCase  ,  _lowerCamelCase  ,  _lowerCamelCase    )
                  components_list.append(_lowerCamelCase    )

      return components_list


if __name__ == "__main__":
 import doctest

 doctest.testmod()





