






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      UpperCamelCase (				unittest.TestCase ):







		"""simple docstring"""



		@slow
		def       A(  self		:       Optional[Any]		)    ->   List[str]:


						'''simple docstring'''
						A	 =      TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'		)
						A	 =      AutoTokenizer.from_pretrained('google/mt5-small'		)

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

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

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













def      snake_case		(							UpperCAmelCase	:       int   ):
				A	 =      0
				while num > 0:
								digit_sum += num % 10
								num //= 10
				return digit_sum







def      snake_case		(							UpperCAmelCase	:       int = 1_00   ):
				A	 =      1
				A	 =      2

				for i in range(2,						max_n + 1   ):
								A	 =      pre_numerator
								A	 =      2 * i // 3 if i % 3 == 0 else 1
								A	 =      cur_numerator
								A	 =      e_cont * pre_numerator + temp

				return sum_digits(UpperCAmelCase   )


if __name__ == "__main__":
					print(f'''{solution() = }''')





