


'''simple docstring'''



import math
import sys







def       _UpperCamelCase (						UpperCamelCase__   ):
			if number != int(UpperCamelCase__   ):
						raise ValueError("""the value of input must be a natural number"""   )
			if number < 0:
						raise ValueError("""the value of input must not be a negative number"""   )
			if number == 0:
						return 1
			UpperCAmelCase__					:   Optional[Any]       				=       [-1] * (number + 1)
			UpperCAmelCase__					:   Union[str, Any]       				=       0
			for i in range(1	, number + 1   ):
						UpperCAmelCase__					:   List[str]       				=       sys.maxsize
						UpperCAmelCase__					:   int       				=       int(math.sqrt(UpperCamelCase__   )   )
						for j in range(1	, root + 1   ):
									UpperCAmelCase__					:   int       				=       1 + answers[i - (j**2)]
									UpperCAmelCase__					:   Optional[int]       				=       min(UpperCamelCase__	, UpperCamelCase__   )
						UpperCAmelCase__					:   str       				=       answer
			return answers[number]


if __name__ == "__main__":
		import doctest

		doctest.testmod()



'''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)