




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 						A(		unittest.TestCase     ):



 '''simple docstring'''


 @slow
 def        a__				(       self      :						int						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'						)
       lowerCamelCase_	  =					AutoTokenizer.from_pretrained('google/mt5-small'						)

       lowerCamelCase_	  =					tokenizer('Hello there'		,							return_tensors='tf'						).input_ids
       lowerCamelCase_	  =					tokenizer('Hi I am'		,							return_tensors='tf'						).input_ids

       lowerCamelCase_	  =					model(A_		,							labels=A_						).loss
       lowerCamelCase_	  =					-tf.math.reduce_mean(A_						).numpy()

       lowerCamelCase_	  =					-21.228168
       self.assertTrue(abs(mtf_score - EXPECTED_SCORE						) < 2E-4						)









def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



      lowerCamelCase_	  =					0
      for i in range(1			,							10_01  ):
            total += i**i
      return str(lowercase  )[-10:]


if __name__ == "__main__":
 print(solution())



