


'''simple docstring'''



__A		={str(digit): digit**5 for digit in range(10)}







def       _UpperCamelCase (						UpperCamelCase__   ):
			return sum(DIGITS_FIFTH_POWER[digit] for digit in str(UpperCamelCase__   )   )







def       _UpperCamelCase (						):
			return sum(
			    number
			    for number in range(1_0_0_0	, 1_0_0_0_0_0_0   )
			    if number == digits_fifth_powers_sum(UpperCamelCase__   )   )


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



'''simple docstring'''



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 						_snake_case   (       unittest.TestCase		):




					@slow
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small""")
								UpperCAmelCase__					:   str       				=       AutoTokenizer.from_pretrained("""google/mt5-small""")

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

								UpperCAmelCase__					:   List[Any]       				=       model(_lowerCamelCase							,							labels=_lowerCamelCase).loss
								UpperCAmelCase__					:   Tuple       				=       -tf.math.reduce_mean(_lowerCamelCase).numpy()

								UpperCAmelCase__					:   Optional[int]       				=       -21.228168
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 2e-4)