


'''simple docstring'''



def       _UpperCamelCase (						UpperCamelCase__ = 1	, UpperCamelCase__ = 1_0_0_0   ):
			UpperCAmelCase__					:   List[Any]       				=       1
			UpperCAmelCase__					:   Optional[Any]       				=       0

			for divide_by_number in range(UpperCamelCase__	, digit + 1   ):
						UpperCAmelCase__					:   list[int]       				=       []
						UpperCAmelCase__					:   Union[str, Any]       				=       numerator
						for _ in range(1	, digit + 1   ):
									if now_divide in has_been_divided:
												if longest_list_length < len(UpperCamelCase__   ):
															UpperCAmelCase__					:   int       				=       len(UpperCamelCase__   )
															UpperCAmelCase__					:   List[str]       				=       divide_by_number
									else:
												has_been_divided.append(UpperCamelCase__   )
												UpperCAmelCase__					:   Optional[int]       				=       now_divide * 1_0 % divide_by_number

			return the_digit


# Tests
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)