


import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer







def  SCREAMING_SNAKE_CASE_		(    __A				:    Optional[Any]						,					__A				:    Any						,					**__A				:    List[str]       )			->	List[str]:

       """simple docstring"""

       a_      :	Dict							=						AutoConfig.from_pretrained(a__						,					**a__       )
       a_      :	Dict							=						AutoModelForSeqaSeqLM.from_config(a__       )
       model.save_pretrained(a__       )
       AutoTokenizer.from_pretrained(a__       ).save_pretrained(a__       )
       return model


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






import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(lowercase__    )		,					'''Tatoeba directory does not exist.'''    )
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      @cached_property
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    List[Any]:
             a_      :	Optional[Any]							=						tempfile.mkdtemp()
             return TatoebaConverter(save_dir=SCREAMING_SNAKE_CASE__  )
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Optional[Any]:
             self.resolver.convert_models(['heb-eng']  )

      @slow
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Any:
             a_							,	a_      :	Dict							=						self.resolver.write_model_card('opus-mt-he-en'  ,							dry_run=SCREAMING_SNAKE_CASE__  )
             assert mmeta["long_pair"] == "heb-eng"


