



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 							lowerCAmelCase__  (	unittest.TestCase						):







					@slow
					def  _lowercase			(       self      :     Any):
											A__	:							List[str]						    = TFAutoModelForSeqaSeqLM.from_pretrained("google/mt5-small")
											A__	:							Optional[Any]						    = AutoTokenizer.from_pretrained("google/mt5-small")

											A__	:							Optional[int]						    = tokenizer("Hello there"			,		return_tensors="tf").input_ids
											A__	:							Union[str, Any]						    = tokenizer("Hi I am"			,		return_tensors="tf").input_ids

											A__	:							int						    = model(_A			,		labels=_A).loss
											A__	:							Union[str, Any]						    = -tf.math.reduce_mean(_A).numpy()

											A__	:							Union[str, Any]						    = -21.22_81_68
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 2e-4)




def 	snake_case__       (					__lowercase       )		->		int:







						"""simple docstring"""

						if divisor % 5 == 0 or divisor % 2 == 0:
												return 0
						A__	:							Tuple						    = 1
						A__	:							Union[str, Any]						    = 1
						while repunit:
												A__	:							Union[str, Any]						    = (1_0 * repunit + 1) % divisor
												repunit_index += 1
						return repunit_index
def 	snake_case__       (					__lowercase = 1_0_0_0_0_0_0       )		->		int:







						"""simple docstring"""

						A__	:							Dict						    = limit - 1
						if divisor % 2 == 0:
												divisor += 1
						while least_divisible_repunit(__lowercase       ) <= limit:
												divisor += 2
						return divisor


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