



'''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


a__						:			Union[str, Any]										=			get_logger(__name__)







def _lowercase   (  __A							,__A       ):



 '''simple docstring'''



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







def _lowercase   (  __A       ):



 '''simple docstring'''



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







def _lowercase   (  __A       ):



 '''simple docstring'''



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







def _lowercase   (  __A							,__A							,__A       ):



 '''simple docstring'''



 __UpperCamelCase				      =  AutoModelForSeqaSeqLM.from_pretrained(__snake_case       )
 if os.path.isfile(__snake_case       ):
  __UpperCamelCase				      =  [pl_ckpt_path]
 else:
  assert os.path.isdir(__snake_case       )
  __UpperCamelCase				      =  list(Path(__snake_case       ).glob("""*.ckpt"""       )       )
  assert ckpt_files, f"could not find any ckpt files inside the {pl_ckpt_path} directory"

 if len(__snake_case       ) > 1:
  logger.info(f"averaging the weights of {ckpt_files}"       )

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

 __UpperCamelCase      ,		__UpperCamelCase				      =  hf_model.load_state_dict(__snake_case							,strict=__snake_case       )
 assert not missing, f"missing keys: {missing}"
 hf_model.save_pretrained(__snake_case       )
 try:
  __UpperCamelCase				      =  AutoTokenizer.from_pretrained(__snake_case       )
  tok.save_pretrained(__snake_case       )
 except Exception:
  pass
  # dont copy tokenizer if cant


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










'''simple docstring'''



a__						:			Any										=			{
    '0000': '0',
    '0001': '1',
    '0010': '2',
    '0011': '3',
    '0100': '4',
    '0101': '5',
    '0110': '6',
    '0111': '7',
    '1000': '8',
    '1001': '9',
    '1010': 'a',
    '1011': 'b',
    '1100': 'c',
    '1101': 'd',
    '1110': 'e',
    '1111': 'f',
}







def _lowercase   (  __A       ):



 '''simple docstring'''



 __UpperCamelCase				      =  str(__A       ).strip()

 # Exceptions
 if not binary_str:
  raise ValueError("""Empty string was passed to the function"""       )
 __UpperCamelCase				      =  binary_str[0] == """-"""
 __UpperCamelCase				      =  binary_str[1:] if is_negative else binary_str
 if not all(char in """01""" for char in binary_str       ):
  raise ValueError("""Non-binary value was passed to the function"""       )

 __UpperCamelCase				      =  (
     """0""" * (4 * (divmod(len(__A       )							,4       )[0] + 1) - len(__A       )) + binary_str
 )

 __UpperCamelCase				      =  []
 for x in range(0							,len(__A       )							,4       ):
  hexadecimal.append(BITS_TO_HEX[binary_str[x : x + 4]]       )
 __UpperCamelCase				      =  """0x""" + """""".join(__A       )

 return "-" + hexadecimal_str if is_negative else hexadecimal_str


if __name__ == "__main__":
 from doctest import testmod

 testmod()





