
def 			UpperCamelCase					(    __lowerCamelCase     : int    ):






       if not isinstance(_lowerCAmelCase				,					_lowerCAmelCase    ):
              raise TypeError("only integers accepted as input"    )
       else:
              snake_case       :   Dict							=      str(abs(_lowerCAmelCase    )    )
              snake_case       :   Tuple							=      [list(_lowerCAmelCase    ) for char in range(len(_lowerCAmelCase    )    )]
              for index in range(len(_lowerCAmelCase    )    ):
                     num_transpositions[index].pop(_lowerCAmelCase    )
              return max(
                  int("".join(list(_lowerCAmelCase    )    )    ) for transposition in num_transpositions    )


if __name__ == "__main__":
   __import__("""doctest""").testmod()










from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
     import tensorflow as tf

     from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM






@require_tf
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""		)
        A :       Dict          =   AutoTokenizer.from_pretrained("""google/mt5-small"""		)

        A :       Tuple          =   tokenizer("""Hello there""",		return_tensors="""tf"""		).input_ids
        A :       Optional[int]          =   tokenizer("""Hi I am""",		return_tensors="""tf"""		).input_ids

        A :       List[Any]          =   model(lowerCamelCase__,		labels=lowerCamelCase__		).loss
        A :       str          =   -tf.math.reduce_mean(lowerCamelCase__		).numpy()

        A :       Tuple          =   -21.22_8168
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 2e-4		)


