def SCREAMING_SNAKE_CASE (   lowercase_:		float ):
       return -num if num < 0 else num


def SCREAMING_SNAKE_CASE (   lowercase_:		list[int] ):
       if len(lowercase_ ) == 0:
              raise ValueError('abs_min() arg is an empty sequence' )
       _UpperCamelCase   :				Optional[Any]       =     x[0]
       for i in x:
              if abs_val(lowercase_ ) < abs_val(lowercase_ ):
                     _UpperCamelCase   :				List[Any]       =     i
       return j


def SCREAMING_SNAKE_CASE (   lowercase_:		list[int] ):
       if len(lowercase_ ) == 0:
              raise ValueError('abs_max() arg is an empty sequence' )
       _UpperCamelCase   :				int       =     x[0]
       for i in x:
              if abs(lowercase_ ) > abs(lowercase_ ):
                     _UpperCamelCase   :				List[Any]       =     i
       return j


def SCREAMING_SNAKE_CASE (   lowercase_:		list[int] ):
       if len(lowercase_ ) == 0:
              raise ValueError('abs_max_sort() arg is an empty sequence' )
       return sorted(lowercase_						,  key=lowercase_ )[-1]


def SCREAMING_SNAKE_CASE (   ):
       assert abs_val(0 ) == 0
       assert abs_val(34 ) == 34
       assert abs_val(-100000000000 ) == 100000000000

       _UpperCamelCase   :				int       =     [-3, -1, 2, -11]
       assert abs_max(lowercase_ ) == -11
       assert abs_max_sort(lowercase_ ) == -11
       assert abs_min(lowercase_ ) == -1


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 test_abs_val()
 print(abs_val(-34))  # --> 34




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


UpperCAmelCase							= get_logger(__name__)


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


def SCREAMING_SNAKE_CASE (   lowercase_:		Union[str, Any] ):
       return {remove_prefix(lowercase_						,  'model.' ): v for k, v in sd.items()}


def SCREAMING_SNAKE_CASE (   lowercase_:		List[Dict[str, torch.Tensor]] ):
       _UpperCamelCase   :				Union[str, Any]       =     {}
       for k in state_dicts[0].keys():
              _UpperCamelCase   :				Tuple       =     [sd[k] for sd in state_dicts]
              _UpperCamelCase   :				str       =     sum(lowercase_ ) / len(lowercase_ )
              assert isinstance(lowercase_						,  torch.Tensor )
              _UpperCamelCase   :				Dict       =     new_t
       return new_sd


def SCREAMING_SNAKE_CASE (   lowercase_:		str						,  lowercase_:		str						,  lowercase_:		str ):
       _UpperCamelCase   :				Optional[Any]       =     AutoModelForSeqaSeqLM.from_pretrained(lowercase_ )
       if os.path.isfile(lowercase_ ):
              _UpperCamelCase   :				Any       =     [pl_ckpt_path]
       else:
              assert os.path.isdir(lowercase_ )
              _UpperCamelCase   :				str       =     list(Path(lowercase_ ).glob('*.ckpt' ) )
              assert ckpt_files, F"could not find any ckpt files inside the {pl_ckpt_path} directory"

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

       _UpperCamelCase   :				Tuple       =     [sanitize(torch.load(lowercase_						,  map_location='cpu' )['state_dict'] ) for x in ckpt_files]
       _UpperCamelCase   :				Optional[Any]       =     average_state_dicts(lowercase_ )

       _UpperCamelCase  ,					_UpperCamelCase   :				Tuple       =     hf_model.load_state_dict(lowercase_						,  strict=lowercase_ )
       assert not missing, F"missing keys: {missing}"
       hf_model.save_pretrained(lowercase_ )
       try:
              _UpperCamelCase   :				str       =     AutoTokenizer.from_pretrained(lowercase_ )
              tok.save_pretrained(lowercase_ )
       except Exception:
              pass
              # dont copy tokenizer if cant


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



