




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						:		int          =						get_logger(__name__)





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__   )     ->     List[Any]:







				'''simple docstring'''


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





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     Tuple:







				'''simple docstring'''


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





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     Optional[Any]:







				'''simple docstring'''


				__lowerCAmelCase					=  {}
				for k in state_dicts[0].keys():
								__lowerCAmelCase					=  [sd[k] for sd in state_dicts]
								__lowerCAmelCase					=  sum(UpperCamelCase__   ) / len(UpperCamelCase__   )
								assert isinstance(UpperCamelCase__						,			torch.Tensor   )
								__lowerCAmelCase					=  new_t
				return new_sd





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







				'''simple docstring'''


				__lowerCAmelCase					=  AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__   )
				if os.path.isfile(UpperCamelCase__   ):
								__lowerCAmelCase					=  [pl_ckpt_path]
				else:
								assert os.path.isdir(UpperCamelCase__   )
								__lowerCAmelCase					=  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}'''   )

				__lowerCAmelCase					=  [sanitize(torch.load(UpperCamelCase__						,			map_location="""cpu"""   )["""state_dict"""]   ) for x in ckpt_files]
				__lowerCAmelCase					=  average_state_dicts(UpperCamelCase__   )

				__lowerCAmelCase	,				__lowerCAmelCase					=  hf_model.load_state_dict(UpperCamelCase__						,			strict=UpperCamelCase__   )
				assert not missing, F'''missing keys: {missing}'''
				hf_model.save_pretrained(UpperCamelCase__   )
				try:
								__lowerCAmelCase					=  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)









from typing import TYPE_CHECKING

from ...utils import _LazyModule


__lowerCAmelCase									=				{'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
 from .tokenization_phobert import PhobertTokenizer

else:
 import sys

 __lowerCAmelCase									=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


