
'''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


__SCREAMING_SNAKE_CASE  =			get_logger(__name__)
def 				__magic_name__(					UpperCAmelCase__   :					str       ,							UpperCAmelCase__   :					str  ):
      if text.startswith(UpperCAmelCase__  ):
            return text[len(UpperCAmelCase__  ) :]
      return text  # or whatever
def 				__magic_name__(					UpperCAmelCase__   :					Dict  ):
      return {remove_prefix(UpperCAmelCase__       ,							"""model."""  ): v for k, v in sd.items()}
def 				__magic_name__(					UpperCAmelCase__   :					List[Dict[str, torch.Tensor]]  ):
      UpperCamelCase_    :  Any				    =    {}
      for k in state_dicts[0].keys():
            UpperCamelCase_    :  Union[str, Any]				    =    [sd[k] for sd in state_dicts]
            UpperCamelCase_    :  int				    =    sum(UpperCAmelCase__  ) / len(UpperCAmelCase__  )
            assert isinstance(UpperCAmelCase__       ,							torch.Tensor  )
            UpperCamelCase_    :  Dict				    =    new_t
      return new_sd
def 				__magic_name__(					UpperCAmelCase__   :					str       ,							UpperCAmelCase__   :					str       ,							UpperCAmelCase__   :					str  ):
      UpperCamelCase_    :  Union[str, Any]				    =    AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase__  )
      if os.path.isfile(UpperCAmelCase__  ):
            UpperCamelCase_    :  Optional[int]				    =    [pl_ckpt_path]
      else:
            assert os.path.isdir(UpperCAmelCase__  )
            UpperCamelCase_    :  int				    =    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}"""  )

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

      UpperCamelCase_    :  Dict				    =    hf_model.load_state_dict(UpperCAmelCase__       ,							strict=UpperCAmelCase__  )
      assert not missing, f"""missing keys: {missing}"""
      hf_model.save_pretrained(UpperCAmelCase__  )
      try:
            UpperCamelCase_    :  Optional[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)






'''simple docstring'''


import warnings

from .generation import GenerationMixin




class 						__snake_case (						_a  ):
		# warning at import time
		warnings.warn(
		    "Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will "
		    "be removed in Transformers v5. Import as `from transformers import GenerationMixin` instead."				, _a				, )




